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Christy Newman
James MacGibbon
Anthony K J Smith
Timothy Broady
Deborah Lupton
Mark Davis
Brandon Bear
Nicky Bath
Daniel Comensoli
Teddy Cook
Elizabeth Duck-Chong
Jeanne Ellard
Jules Kim
John Rule
Martin Holt
Understanding trust in digital health Understanding trust in digital health
among communities aected by among communities aected by
BBVs and STIs in AustraliaBBVs and STIs in Australia
Centre for Social Research in Health
UNSW Sydney NSW 2052 Australia
T +61 2 9385 6776
F +61 2 9385 6455
E csrh@unsw.edu.au
W www.arts.unsw.edu.au/csrh
© UNSW Sydney 2020
Suggested citation:
Newman, C., MacGibbon, J., Smith, A. K. J., Broady, T., Lupton, D., Davis, M., Bear, B., Bath, N., Comensoli,
D., Cook, T., Duck-Chong, E., Ellard, J., Kim, J., Rule, J., & Holt, M. (2020). Understanding trust in digital
health among communities affected by BBVs and STIs in Australia. Sydney: UNSW Centre for Social
Research in Health. http://doi.org/10.26190/5f6d72f17d2b5
UNSW Centre for Social Research in Health
Understanding trust in digital health among communities affected by BBVs and STIs in Australia I
Acknowledgements
Many thanks to all of those who took part in the survey or interviews for this project, generously
sharing their perspectives and experiences with us. Thanks also to all of the Chief Investigators
on the project (Christy Newman, Martin Holt, James MacGibbon, Timothy Broady, Mark Davis
and Deborah Lupton) and those who represented our partner organisations (Jeanne Ellard,
John Rule, Jules Kim, Nicky Bath, Daniel Comensoli, Teddy Cook, Brandon Bear, Elizabeth Duck-
Chong). Essential research support and expertise was also provided by Anthony K J Smith
at CSRH, particularly in preparing the ethics application, analysing the qualitative data, and
contributing to publications. Professor Michael Kidd AM also provided expert input into the
design of this study.
We are indebted to the following people who provided us with assistance through piloting the
survey, or promoting the survey in our target communities (in alphabetical order): Craig Andrews,
Teddy Bell, Andrew Buchanan, Hilary Caldwell, Elvis Caus, Sione Crawford, Jane Costello, Misty
Farquhar, Lauren Foy, Neil Fraser, Kirsty Machon, Susan McGuckin, Hayden Moon, Hannah
Morgan, Lionel Rabie, Katy Roy, Teresa Savage, Jake Rance, Gala Vanting, Son Vivienne, Michael
Wacher, Andrea Waling, Warner Ward, Emma Williams, Sue Wood.
This research was supported by the Australian Government Department of Health. The
views expressed herein are those of the authors and do not represent those of the Australian
Government. The contributions of the UNSW-based investigator team were supported by the
Centre for Social Research in Health and the Social Policy Research Centre, both of which
receive support from UNSW Arts and Social Sciences. Mark Davis was supported by Monash
University.
UNSW Centre for Social Research in Health
Understanding trust in digital health among communities affected by BBVs and STIs in Australia II
Executive summary 1
Background 3
Methods 7
Findings: Key informant interviews 9
Promiseandbenetsofdigitalhealth 9
Risks and consequences of digital health 10
Trust for different priority populations 11
Mechanisms and conditions to establish trust in digital health 13
Findings: Community survey 15
Participant characteristics and internet access 15
Health and wellbeing 17
Trust 18
My Health Record 20
Digital technologies and services 23
Novel coronavirus (COVID-19) measures 24
Free text responses 25
Discussion 27
References 32
Appendices 39
Appendix A: Key informant interview methods 39
Appendix B: Community survey methods 41
Appendix C: Participant characteristics and internet access 43
Appendix D: Health and wellbeing 47
Appendix E: Trust 54
Appendix F: My Health Record 61
Appendix G: Digital technologies and services 79
Appendix H: Novel coronavirus (COVID-19) items 92
Appendix I: Limitations 94
Contents
UNSW Centre for Social Research in Health
Understanding trust in digital health among communities affected by BBVs and STIs in Australia III
TableA1. Demographicandprofessionalproleofkeyinformantparticipants 40
Table C1. Participant characteristics among Trust in Digital Health Survey participants, by
priority population group 43
Table C2. Cross tabulations showing frequencies in which priority population members
were members of one or more priority population groups 45
Table C3. Internet access among Trust in Digital Health Survey participants, by priority
population group 46
Table D1. Access to government subsidised health care among Trust in Digital Health
Survey participants, by priority population group 47
Table D2. Health and wellbeing status among Trust in Digital Health Survey participants,
by priority population group 47
Table D3. Reported experiences of stigma among Trust in Digital Health Survey priority
population groups in the last 12 months 50
Table E1. Generalised trust and trust in institutions among Trust in Digital Health Survey
participants, by priority population group 54
TableE2. Trustinhealthcareservicestokeepmedicalinformationprivateandcondential
among Trust in Digital Health Survey participants, by priority population group 56
Table F1. Access to and knowledge about My Health Record among Trust in Digital Health
Survey participants, by priority population group 61
Table F2. Use of My Health Record among Trust in Digital Health Survey participants who
had a record, by priority population group 62
Table F3. Reasons for opting out of My Health Record and information that might have
been useful among Trust in Digital Health Survey participants who had opted
out of My Health Record or deleted their record, by priority population group 64
Table F4. Willingness to share My Health Record data with health services among Trust in
Digital Health Survey participants, by priority population group 65
Table F5. Willingness to share My Health Record data with government agencies and
industry among Trust in Digital Health Survey participants, by priority population
group 67
TableF6. Supportforsharingde-identiedhealthdataforresearchamongTrustinDigital
Health Survey participants, by priority population group 68
Table G1. Use of websites by participants to manage health or share health information
and trust in these platforms (if used) to manage the security and privacy of their
information, by priority population group 79
List of tables
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia IV
Table G2. Use of smartphone or wearable device apps by participants to manage health
or share health information and trust in these platforms (if used) to manage the
security and privacy of their information, by priority population group 80
Table G3. Use of other online services by participants to manage health or share health
information and trust in these platforms (if used) to manage the security and
privacy of their information, by priority population group 81
Table G4. Perceived importance of digital technologies among participants to manage or
promote their health or the health of someone they care for, by priority
population group 82
Table G5. Willingness to share data from a smartphone or wearable device app with health
services among Trust in Digital Health Survey participants, by priority population
group 83
Table G6. Willingness to share data from a smartphone or wearable device app with
government agencies and industry among Trust in Digital Health Survey
participants, by priority population group 85
Table H1. Changes in behaviour or views among Trust in Digital Health Survey participants
in response to COVID-19, by priority population group 92
Table H2. Information that participants of the Trust in Digital Health Survey would be
willing to share with health authorities to help the response to COVID-19, by
priority population group 93
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia V
List of figures
Figure D1. Factors associated with having one or more long-term medical conditions
among Trust in Digital Health Survey participants (N=2,240) 51
Figure D2. Factors associated with poor/fair health (self-rated) among Trust in Digital
Health Survey participants (N=2,240) 51
Figure D3. Factors associated with receiving mental health care among Trust in Digital
Health Survey participants (N=2,240) 52
Figure D4. Factors associated with taking four or more prescription medications among
Trust in Digital Health Survey participants (N=2,240) 52
Figure D5. Factors associated with good/excellent access to health care (e.g. affordability/
location) among Trust in Digital Health Survey participants (N=2,240) 53
Figure E1. Factors associated with generalised trust (‘Most people can be trusted’)
among Trust in Digital Health Survey participants (n=2,239) 58
Figure E2. Factors associated with trust in general practice (GP) services to keep
informationprivateandcondentialamongTrustinDigitalHealthSurvey
participants who had attended a GP in the past year (n=1,986) 58
Figure E3. Factors associated with trust in pharmacies to keep information private and
condentialamongTrustinDigitalHealthSurveyparticipantswhohad
attended a pharmacy in the past year (n=1,546) 59
Figure E4. Factors associated with trust in dentists to keep information private and
condentialamongTrustinDigitalHealthSurveyparticipantswhohad
attended a dentist in the past year (n=1,042) 59
Figure E5. Factors associated with trust in in-patient hospitals to keep information
privateandcondentialamongTrustinDigitalHealthSurveyparticipantswho
had attended an in-patient hospital in the past year (n=321) 60
Figure E6. Factors associated with trust in out-patient hospitals and specialist clinics to
keepinformationprivateandcondentialamongTrustinDigitalHealthSurvey
participants who had attended an out-patient hospital or a specialist clinic in
the past year (n=437) 60
Figure F1. Factors associated with opting out of or deleting My Health Record among
Trust in Digital Health Survey participants (N=2,240) 69
Figure F2. Factors associated with greater knowledge about My Health Record among
Trust in Digital Health Survey participants (N=2,240) 69
Figure F3. Factors associated with learning about My Health Record from information
provided by community organisations among Trust in Digital Health
Survey participants who knew about My Health Record (n=1,936) 70
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia VI
Figure F4. Factors associated with participants opting out of or deleting My Health Record
due to concern that health professionals would not always treat them with
dignity and care (n=656) 70
Figure F5. Factors associated with participants opting out of or deleting My Health Record
due to concern that the government could not adequately protect their privacy
(n=656) 71
Figure F6. Factors associated with participants opting out of or deleting My Health Record
due to concern that their data may be used for commercial purposes (n=656) 71
Figure F7. Factors associated with participants opting out of or deleting My Health Record
due to concern that their data may be used for research without their consent
(n=656) 72
Figure F8. Factors associated with participants opting out of or deleting My Health Record
due to concern that their data may be shared between government agencies
without their consent (n=656) 72
Figure F9. Factors associated with participants opting out of or deleting My Health Record
due to concern that their medical information may be hacked or leaked (n=656) 73
Figure F10. Factors associated with participants opting out of or deleting My Health Record
due to concern that their data may be used by the government in ways that
disadvantage them (n=656) 73
Figure F11. Factors associated with participants opting out of or deleting My Health Record
because their doctor told them they should opt out (n=656) 74
Figure F12. Factors associated with participants opting out of or deleting My Health Record
because another person or organisation told them they should opt out (n=656) 74
Figure F13. Factors associated with willingness to share relevant information from an
electronic health record with general practice (GP) services among participants
who were eligible for a record (n=2,155) 75
Figure F14. Factors associated with willingness to share relevant information from an
electronic health record with pharmacies among participants who were eligible
for a record (n=2,149) 75
Figure F15. Factors associated with willingness to share relevant information from an
electronic health record with dentists among participants who were eligible for
a record (n=2,152) 76
Figure F16. Factors associated with willingness to share relevant information from an
electronic health record with in-patient hospitals among participants who were
eligible for a record (n=2,150) 76
Figure F17. Factors associated with willingness to share relevant information from an
electronic health record with out-patient hospitals or specialist clinics among
participants who were eligible for a record (n=2,149) 77
Figure F18. Factors associated with willingness to share relevant information from an
electronic health record with health-related government agencies among
participants who were eligible for a record (n=2,149) 77
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia VII
Figure F19. Factors associated with willingness to share relevant information from an
electronic health record with non-health-related government agencies among
participants who were eligible for a record (n=2,149) 78
Figure F20. Factors associated with willingness to share relevant information from an
electronic health record with insurance companies among participants who
were eligible for a record (n=2,147) 78
Figure F21. Factors associated with willingness to share relevant information from an
electronic health record with law enforcement among participants who were
eligible for a record (n=2,149) 79
Figure F22. Factors associated with willingness to share relevant information from an
electronichealthrecordwithbanksornancialinstitutionsamongparticipants
who were eligible for a record (n=2,146) 79
Figure G1. Factors associated with willingness to share health data from a smartphone or
wearable device app with general practice (GP) services among Trust in Digital
Health Survey participants (n=2,233) 87
Figure G2. Factors associated with willingness to share health data from a smartphone or
wearable device app with pharmacies among Trust in Digital Health Survey
participants (n=2,230) 87
Figure G3. Factors associated with willingness to share health data from a smartphone or
wearable device app with dentists among Trust in Digital Health Survey
participants (n=2,229) 88
Figure G4. Factors associated with willingness to share health data from a smartphone or
wearable device app with in-patient hospitals among Trust in Digital Health
Survey participants (n=2,231) 88
Figure G5. Factors associated with willingness to share health data from a smartphone or
wearable device app with out-patient hospitals or specialist clinics among
Trust in Digital Health Survey participants (n=2,225) 89
Figure G6. Factors associated with willingness to share health data from a smartphone or
wearable device app with health-related government agencies among Trust
in Digital Health Survey participants (n=2,231) 89
Figure G7. Factors associated with willingness to share health data from a smartphone or
wearable device app with non-health-related government agencies among Trust
in Digital Health Survey participants (n=2,233) 90
Figure G8. Factors associated with willingness to share health data from a smartphone or
wearable device app with insurance companies among Trust in Digital Health
Survey participants (n=2,230) 90
Figure G9. Factors associated with willingness to share health data from a smartphone or
wearable device app with law enforcement among Trust in Digital Health Survey
participants (n=2,232) 91
Figure G10. Factors associated with willingness to share health data from a smartphone or
wearabledeviceappwithbanksornancialinstitutionsamongTrustinDigital
Health Survey participants (n=2,232) 91
UNSW Centre for Social Research in Health
Understanding trust in digital health among communities affected by BBVs and STIs in Australia 1
Executive summary
Despite extensive government investment to expand digital health, minimal research has been
conducted on community views of these systems in Australia. In particular, there has been
scant attention to the perspectives on digital health of populations affected by blood-borne
viruses (BBVs) and sexually transmissible infections (STIs) has received little attention.
The Trust in Digital Health study was conducted by the Centre for Social Research in Health
in partnership with community organisations representing four of the priority populations in
the current national BBV/STI strategies: people with HIV, trans and gender diverse people, sex
workers, and gay and bisexual men.
Our methods included a national, online cross-sectional survey (April–June 2020) of the general
population,includingspecicrecruitmenttargetsforthefourprioritypopulations.Wealso
conducted semi-structured interviews with key informants (March–June 2020) with expertise in
communities affected by BBVs/STIs, stigma and marginalisation.
Thesurveysampleincluded2,240eligibleparticipants,including600(26.8%)classiedas
members of one or more priority populations. Overall, priority populations reported the lowest
levels of trust in digital technologies and in some health care services, and the most frequent
experiences of stigma.
PrioritypopulationsweremorelikelytounderstandthepotentialbenetsofMyHealthRecord,
but also to have opted out of having one. These groups were also more likely to have made
use of digital services to access essential health care and medications during the COVID-19
response, and the least likely to be willing to share personal information with health authorities.
Keyinformantswerekeenlyawareofthepromiseandbenetsofdigitalhealth,butalso
concerned about the risks and consequences of communities affected by BBVs/STIs engaging
withthesesystems.Specicissuesrelatedtodifferentpopulations,buttherewasashared
focus on the harmful impacts of experiencing stigma and discrimination in health settings. Key
informants also consistently reported that these communities typically fear that their personal
information is more easily shared through digital means without the consent of the affected
person, with a range of potential social, legal and economic consequences.
A range of mechanisms and conditions for building trust in digital health were also discussed,
includingtheneedforsignicantreformsinsystemdesign,incommunityconsultation
processes, and in the policy and legal contexts that shape the everyday lives, rights and
wellbeing of these communities.
Thevarietyofevidencewecollectedsuggeststhattrustindigitalhealthisinuencedless
by technical design or digital literacy, and more by the relational and structural factors which
underpin trust in the institutions responsible for health system design and regulation.
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 2
Toaddresstheseconcerns,werecommendndingnewandmoreeffectivewaystoensurethat
consentissecuredtocollect,storeandsharehealthdata,andthatconsentisspecic,dynamic,
and informed. Major investments in discrimination reduction strategies at every level of the
health care system are also necessary to ensure that health care is accessible, competent, and
safe. Resources should be directed towards remediating the legal and policy conditions that
continue to discourage some communities from participating in digital health, and in supporting
meaningful consultation with peer-based organisations who have the trust of communities
affected by BBVs and STIs.
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 3
Background
Acommonunderstandingoftrustisthatitinvolvesa‘rmbelief[orcondenceorfaith]inthe
reliability, truth, or ability of someone or something’ (OED Online, 2020). In social research, trust
has been described as the ‘glue that holds society together’ (Ward et al., 2014, p. 1), making it
possible for us to go about our daily lives, rather than living in constant uncertainty (Luhmann,
1979). However, trust also ‘can no longer be assumed, it is conditional and has to be earned’
(Calnan&Rowe,2008,p.101),negotiatedasaqualityofrelationships,overtime,andinspecic
contexts (Camporesi et al., 2017). Digital health systems – which often require people to share
personal information in abstract or remote systems – have the potential to complicate and
intensify the important question of what conditions need to be met in order to earn and sustain
community trust.
Digital technologies for supporting or promoting health – including electronic health
records, mobile apps, social media sites, wearable devices, online forums and virtual health
consultations – have become a key part of the landscape of health care and health promotion
(Lupton, 2018). The promise of digital health is extensive, but often summarised as offering the
potentialformoreeffective,ecientandintegratedhealthsystemswhichcanreducemedical
errors, streamline services, and inform more targeted population health responses (Lupton,
2018). Digital health is an important policy priority for the Australian federal government,
demonstrated through major investments in a national electronic health record system (My
HealthRecord,orMHR),withtheaimofcreatingarecordforeveryAustralian.Thisconrmsa
longstandingstrategiccommitmentto‘digitalinformation[as]thebedrockofhighqualityhealth
care’ (Australian Digital Health Agency (ADHA), 2017, p. 3). In addition to improving health
systemecienciesandstreamlininghealthcaredelivery,MyHealthRecordaimstoprovide
its users with greater oversight of their health care, to help them make medical decisions and
to share health information with all providers involved in their care (Australian Digital Health
Agency, n.d.)
However, a series of controversies relating to the handling of citizens’ personal information in
government systems have also received attention over the past few years, including mistakes
made in the ‘Robo-Debt’ automated welfare surveillance program (Mann, 2020) and the
disruptionoftherstonlinenationalCensusbyhackers(ABCNews,2016).Consumerconcern
about the privacy and security of health data in My Health Record has also been widely reported,
including in the small amount of research conducted on this topic in Australia (Lupton, 2017,
2019). There was a major public outcry driven by privacy and consumer advocates after the
federal government made My Health Record an opt-out, rather than opt-in, system in 2018
(Knaus, 2019). Legal scholars have also challenged the treatment of private information and
patientcondentialityintheimplementationofthesystem(Mendelsonetal.,2018;Mendelson
& Wolf, 2016).
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 4
Looking globally, we can see that although most electronic health record systems require
patients to provide informed consent to sign up and to be provided with a comprehensive
understanding of how their personal health data may be used by providers and third parties,
concernsaboutriskstoprivacyandsecurityremain(Bratanetal.,2010;Fryetal.,2014;
Spriggsetal.,2012).Consumercondencehasbeenunderminedbyinstancesinwhichdigital
information has been leaked or breached due to human error and poor security measures,
commercial use of health information has occurred without knowledge or consent of patients,
and deliberate hacking and stealing of health data by cybercriminals for activities such as
insurancefraudhasbeendetected(Huckvaleetal.,2015;Wicks&Chiauzzi,2015).Somedigital
datasets which appear to be anonymous and to not contain personally identifying information
can be combined to ‘de-anonymise’ health data, thus re-identifying people even if they have been
assured that their privacy will be preserved (Zivanovic, 2014). The linkage of electronic medical
records with public health surveillance registries has been actively resisted by those concerned
with the potential impacts on people with a history of ‘sensitive or stigmatised conditions’
(Rosenblat et al., 2014).
Despite the intensive policy and public attention to these issues, very little social research has
explored community views and practices of engaging with digital health systems, or the factors
thatinuencetrustandmistrustinthesesystems(Andrewsetal.,2014;Harrisonetal.,2018;
Kingetal.,2012;Kraheetal.,2019;Lupton,2019).Researchintheeldofmedicalinternet
studies has explored these issues from the perspective of systems design and policy (Adjekum
et al., 2018), but with limited research capturing the perspectives of diverse communities
themselves.
The lack of an evidence base to inform Australian digital health policy and practice reduces
thepotentialforallcommunitiestobenetfromthepromiseofdigitalhealth.Forexample,the
National Strategies for the prevention of bloodborne viruses (BBVs) and sexually transmissible
infections (STIs) include strong commitments to improving services which rely on digital health
infrastructure,includingpartnernoticationsystems(forSTIsandHIV),improvedpatient
management systems, and increased integration, linking and coordination across care systems
(Australian Department of Health, 2018a, 2018b). However, minimal attention has been paid
to understanding how the populations prioritised in these strategies feel about engaging with
these systems, which is a missed opportunity for ensuring these strategic priorities can be
achieved through effective and acceptable means.
Many of the populations prioritised in the national strategies – including, but not limited to,
sex workers, people with HIV or viral hepatitis, people who use illicit or injecting drugs, gay
and bisexual men, and trans and gender diverse people – are in regular contact with health
services. Although this does not apply to everyone, many in these communities engage with
health services frequently in order to secure access to essential forms of care, such as HIV
treatment,harmreductionservices,sexualhealthcare,orgenderarminghormones.Manyof
these communities also experience persistently poorer health outcomes compared with general
populations(CoutoeCruzetal.,2018;Lyonsetal.,2012;Northrop,2017;Perales,2018;Plattet
al.,2018;Reisneretal.,2016).
These disparities make it even more problematic that many of these groups report pervasive
and repeated experiences of stigma and discrimination in health care settings, and in the
broadercommunity(Broadyetal.,2020;Camaetal.,2018).Insomecases,membersofthese
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 5
communities face serious risks to their capacity to work and parent when personal histories of
drug use or sex work are accessed by health and other government services in jurisdictions in
which those practices are criminalised, including the potential removal of children (Arpa, 2017,
p.7;Maher&Dixon,2017;Mitranietal.,2009;Treloaretal.,2004).Someofthesegroupshave
also expressed concerns about the potential for inadvertent disclosure of their HIV status,
or legal cases being pursued in the context of HIV transmission (Cameron & Rule, 2009).
Because of these concerns, some members of priority populations may adopt strategies that
compartmentalise their health care and related disclosures (Davis & Manderson, 2014), seeing
different providers for different conditions (e.g. sexual health, mental health, general practice,
harmreduction,genderarminghealthcare)andavoidingthesharinginformationbetween
services. This fear of sharing and disclosure may fragment their care and lead to poorer health
outcomes. These communities may have particular fears that their personal health data will not
bemanagedsecurelyandcondentially,orhavedoubtsthatthebenetsofsharingtheirdata
will be realised because of the ways that systems have been designed, or the complexity and
timeinvolvedinengagingmeaningfullywiththosesystems(Marentetal.,2018;Stableinetal.,
2015;Thompson,2016).
Recognising these issues, some of the community organisations who represent the needs
and interests of affected priority populations in Australia have invested considerable time and
resourcesincommunityeducationaboutthebenetsandtherisksofdigitalhealthsystemsfor
thosewithstigmatisedhealthconditionsandidentities(Duxeld,2018;NAPWHA,n.d.;NUAA,
2018;PositiveLifeNSW,2018;ScarletAlliance,2018).Theseactivitiesdemonstratesthatthere
is existing knowledge within communities about digital health systems: their promise, and their
risks. However, there has been little formal research conducted to document community views.
While there has been some investment in improving the inclusion of lesbian, gay and bisexual
identicationsindatacollectionsystemsindifferentcontexts,veryfewstudieshaveexplored
the concerns of those communities regarding the privacy and security of their personal data,
and there also has been very little attention paid to the experiences of trans and gender diverse
people. In focus groups conducted with trans and gender diverse people in the U.S., concerns
wereexpressedabouttheprivacyofgenderidentityandmedicalgenderarmationinformation
in records shared between clinics, health care professionals, and health insurance companies
(Thompson, 2016).
There is more published research on how people with HIV and gay and bisexual men feel about
storing and sharing their health information in digital systems, although most of this has been
conductedoutsideofAustralia(Jacometetal.,2020;Marentetal.,2018;Mootzetal.,2020;
Stablein et al., 2015). This research has indicated mixed views about sharing information in
digital health systems. In one study, gay men in the US reported mixed views about privacy
risks related to electronic health records (Stablein et al., 2015). A survey of people with HIV and
physicians conducted in France found both enthusiasm and scepticism for electronic health
systems (Jacomet et al., 2020). A focus group study in New York City of men who have sex
with men, including a large proportion of Black and Latino men, found that while most were
supportive of the potential for ‘big data’ and technology to reach marginalised populations,
they were also concerned about security issues (Mootz et al., 2020). Through a co-design
process to develop a prospective mobile health platform (e.g. mHealth: health care delivered
through mobile phones) for people with HIV across the EU, Marent et al. (2018) argued that
people with HIV felt ambivalence towards particular aspects of digital health, and that this
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 6
ambivalencewasembeddedinspeciccircumstances(e.g.relationshipswithparticular
cliniciansorunemployment).Theyarguedthatneither‘resistance’or‘acceptance’wassucient
to characterise participants’ views of an mHealth HIV care platform. In the Australian context,
an internal audit reported on the proportion of people with HIV in one clinic who had consented
for their information to be included in regionally shared electronic health records. However, this
study did not explore their reasons for providing consent, or their views on security, privacy or
trust (Farrugia Parsons & Ryder, 2016).
Since the beginning of the COVID-19 pandemic, we have seen an increased attention to
questions of trust in governments and science. Differences in community understandings of,
support for, and capacity to engage with public health recommendations have inspired further
debate about the conditions that shape public trust (Calnan et al., 2020). The pandemic context
has foregrounded telehealth approaches to health care (e.g. the provision of remote health
care through the use of telecommunication technologies), which, like digital health records,
require participants to trust these systems and their reliability (Beard, 2020). Research on
pandemic preparedness has touched upon some of these issues. For example, Davis and
Lohm (2020) argued that the uncertainties of rapidly evolving pandemic crises lead to trust
dilemmas.Forexample,the2009swineupandemicwasatrstaglobalcrisis,butthen
determinedtobeamildinfectionformostpeople;ashiftinriskthatledtothechallengeof
‘falsealarm’ramicationsfortrustinscience.DavisandLohm(2020)explainedthat‘Pandemic
communications, then, are also trust communications. Persuading publics that they might be
at risk and should take action asks them to place trust in experts, their knowledge, and in public
health more generally’ (p. 184).
Asthisbriefbackgroundreviewindicates,itisbothtimelyandsignicanttoinvestigatetrust
in digital health among Australian communities affected by BBVs and STIs. Understanding
theirperspectivesiscriticalforensuringthatallcommunitiesbenetfromthesignicant
investments being made in digital health systems.
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 7
Methods
With funding from the Australian Government Department of Health, the Centre for Social
Research in Health conducted a rapid exploratory study to document community and expert
views on ‘Trust in Digital Health’.
Ethical approval was provided by the UNSW Human Research Ethics Committee (HC191000)
and the ACON Research Ethics Review Committee (RERC201929). Potential participants were
directed to the study website: http://www.trustindigitalhealth.org.au/
The development of study tools and recruitment processes, as well as limitations of the study,
are discussed in the appendices. However, there are a few key points to note here.
This project was framed by the need to understand trust in digital health, broadly, and had the
following aims:
• To interview key informants about the issues which they believe shape trust in digital
health among marginalised communities in Australia, including populations prioritised in
the prevention of HIV, viral hepatitis and STIs
• To conduct a national online survey exploring how Australians store and share health
information online, including targeted recruitment of people with HIV, gay and bisexual
men, sex workers, and trans and gender diverse people
• To generate timely insights regarding how different communities are engaging with digital
health systems today, informed by both qualitative and quantitative data, and identify gaps
in knowledge
Whilewewouldhaveideallyengagedallofthecommunitiesidentiedasprioritypopulations
in the National Strategies relating to blood borne viruses and STIs, we were limited in this
pilot project by time and resources. We therefore focused on engaging a sub-group of priority
populations, with the aim of producing preliminary insights to guide future research on trust in
digital health which would ideally take a more comprehensive approach.
The four priority populations targeted for recruitment in the community survey were: people with
HIV, gay and bisexual men, sex workers, and trans and gender diverse people. The study design
and materials were developed in close collaboration with community organisations representing
these priority populations, including four national organisations – the National Association of
People with HIV Australia, Scarlet Alliance, Australian Federation of AIDS Organisations, and
the National LGBTI Health Alliance – and one state-based organisation, ACON, with expertise
in transgender and gender diverse health equity. We received additional support with survey
recruitment from a number of other organisations, including Positive Life NSW, Pozhet, Bobby
Goldsmith Foundation, Positive Women Victoria, Transgender Victoria and TransFolk of WA.
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 8
To strengthen insights into the issues pertaining to populations not well represented in the
survey, we took care to recruit key informants who could represent a broad a range of expert
perspectives, including those relating to the experiences of people who use illicit and injecting
drugs.
A major focus of survey recruitment was achieving a minimum target (n=100) in each of the
four targeted priority populations. This was achieved, but – as expected – numbers were low
intheotherprioritypopulationgroups.Forthisreason,respondentswhotthecriteriafor
membership only of the priority population groups not targeted (e.g. people who inject drugs,
Aboriginal and Torres Strait Islander people, people with hepatitis B, people with hepatitis C) had
their responses included in the overall analyses, but those characteristics were not included as
independent variables in the multivariate models, as their numbers were too low to generate
reliable results.
Althoughthesamplecanbesplitintotwogroups–allthosewhotthecriteriaformembership
of one or more of the priority population groups (n=600) and all those who are not (i.e. ‘general
population’, n=1,640) – results are presented for each of the four priority populations separately,
as there is little value in collapsing these very different groups together.
Because some participants were members of multiple priority population groups, frequencies
and proportions reported for each priority population in the tables should not be read by
comparing them with the other columns, e.g. as a comparison to (or exclusive from) other
priority populations, nor as a comparison to only the general population sample. For these
analyses,astatisticallysignicantresultforaprioritypopulationgroupshowsthathaving
that characteristic is independently associated with the outcome variable, in comparison to all
participants who did not have that characteristic. This comparison group (or reference category)
may include participants from the general population and other priority populations who did
not have that characteristic. See Appendix B for further details about the interpretation of the
multivariate analyses.
It is also important to note that data collection for this study occurred during the early stages of
theCOVID-19pandemic,whencommunitiesacrossAustraliawereundergoingtherstperiodof
lockdown (beginning in mid-March 2020). Digital technologies became even further embedded
in health service responses during this time, particularly in the extension of Medicare subsidies
for telehealth consultations in some areas, and the fast-tracking of electronic prescribing.
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Findings: Key informant interviews
During March–June 2020, we conducted interviews by phone or teleconferencing software
with 16 Australian key informants holding expertise in policy, advocacy, education, research
and health promotion with one or more of the priority populations in relation to digital health
(see Appendix A). The purpose of the interviews was to identify key issues in engaging these
communities with digital health systems, and a thematic analysis was conducted of the
deidentiedtranscripts.Toensurecondentiality,inthesummarythatfollows,keyinformants
are only referred to by participant number e.g. P01, P02 etc. The overall demographics of the key
informant sample can be found in Appendix A.
For the purpose of this summary report, we present the following four themes from our
preliminary analysis of the key informant interviews. More in-depth analyses of the qualitative
data will be prepared for publication in journal articles.
Promise and benefits of digital health
Keyinformantsdiscussedthepotentialbenetsofdigitalhealth,whichprimarilycentredon
electronic health records, particularly Australia’s My Health Record. It was recognised that My
Health Record had the potential to improve continuity of care, avoid unnecessary repeat tests,
and assist ageing populations with managing chronic and complex health conditions. However,
key informants drew a distinction between the promise of My Health Record and other digital
health innovations, and what had been able to be achieved in practice, either because the
technology had failed to deliver on its promise or because there were too many risks for priority
populationsintryingtosecurethosebenets(seebelow).
Despitethechallengestheyidentied,manykeyinformantswereoptimisticthatdigitalhealth
innovations were important to deliver correctly, through adequate resourcing and appropriate
engagement and governance approaches, especially for priority populations. For example,
keyinformantsarguedthattherewereimportantsocialbenetstobeachievedbyimproving
datacollection(andforsomeprioritypopulations,beingidentiedandcountedproperly),data
linkage, and generating a better understanding of the health care experiences of historically
underrepresented populations. Other aspects of digital health, such as mobile apps and social
media, were viewed as enormously important for priority populations, allowing people to ‘crowd-
source’ answers from peers who were either more accessible or perceived to be safer to access
than mainstream or medical sources of support and information.
Tempering the promise of electronic health records for priority
populations:
[A]single,electronic,healthrecordistheHolyGrail.Youknow,continuityofcare.
Avoid repeated tests. Avoid wrong prescriptions, etc. A no-brainer at perspective.
However, when you look at it more from that of a marginalised, oppressed
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groupfacingminoritystress,thenit’sguredquitedifferently.So,obviously,with
intravenous drug-users or people with HIV, there’s a lot of stigma in that area. And
therefore, they aren’t necessarily gonna want every GP they turn up to know about
thathistory.[P01]
The benefits of priority populations being counted:
There is a huge need for data linkage that can give us a better sense of how big the
population is for different conditions or different demographics with unmet needs.
And on that basis, from that you get things like funding, recognition, inclusion in
research.[P03]
Digital peer connections as supporting health:
Whenyou’renewlydiagnosed[withHIV],sometimesit’sjusteasiertocrowd-source
an answer than it is to like either do the research yourself and sift through all the
fact versus myth, or you’re not that necessarily comfortable about disclosing your
statustoyourdoctor.Andsothosepeeronlinespaceskindofllsagapbetween
where the physical organisations don’t go or can’t go because of their funding or their
limitationswithintheirfundingagreements.[P04]
Risks and consequences of digital health
A key challenge that key informants brought up related to the numerous risks that using digital
health systems posed, especially for priority populations. Some of these risks were speculative,
such as the possibility of health data being linked to other systems or (re)used in unknown
ways. However, key informants also described many instances in which health data had already
been made available to other sectors, services or systems, such as to the police or to health
insurance companies. They also noted occasions in which this had resulted in stigma and
discrimination at health services due to records inadvertently – and usually unnecessarily –
disclosing particular aspects of a person’s personal and health information.
While some of these risks could be mitigated through better design or affordances in the digital
system or technology – such as the option for users to prevent sharing of data in an electronic
health record – many of these issues were structural (e.g. persistent stigma) or problems that
could result from hacking or from unknown, undesirable uses of data in the future.
Key informants also raised issues about the way that digital health systems and apps were
designed, such as systems enforcing a binary gender system for users, or the degree of digital
literacy required to navigate systems, making them inaccessible to some populations.
Data linkage, privacy, and criminalisation of sex work:
If health data can be accessed by the police or by a licencing authority, then obviously
we have a problem with that. So inter-departmental access question is a big one for
us. And that’s a short-term and a long-term question because maybe at the moment
it’saboutcriminalisationbutmaybein15yearsit’llbeaboutcustody[ofchildren].
[P16]
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Speculations about sharing of data and hacking:
If a person was diagnosed with HIV at a clinic operated by one of the major mining
companies,thatwoulddenitelyaffecttheirabilitytoworkfor,intheminerals
industry. And I think people would have a reasonable concern that there would be
informal sharing of that information by clinic staff or HR staff. The problem with any
kind of database is that could be accidentally released or hacked into. There really is
nothingthatyoucandoafterthatriskhaseventuated.[P03]
Future uses of data:
I think that’s what a lot of communities might be picking up on: that there isn’t any
forward thinking. People just develop these apps and then the government develops
theapps,butthenthere’snothingaround,“Okay,well,whathappens,youknow,ve
months down the track? What happens if someone hacks your data?” Like what are
all the safeties that you have in place to enable us to use that app safely? Like that
COVIDtrackingapp,Iwon’tdownloadit.[P11]
Trust for dierent priority populations
Key informants discussed how the conditions affecting trust in digital health differed for the
particular priority populations they worked with, and the types of issues related to digital health
that were important for these populations. A recurring theme across the priority populations
was, however, the pervasive and persistent stigma and discrimination experienced in health
care settings, which meant that people were less likely to trust in the digital systems which were
intended to facilitate communication and connection between these settings. In particular, there
was a shared concern reported by key informants that digital health could lead to the unwanted
disclosure of an aspect of a person’s history or personal life which encouraged health care
practitionerstobecomexatedonoroverlycuriousaboutthatparticularissue,ratherthanthe
health care need at hand.
The example of ‘trans broken arm syndrome’ was mentioned by a few participants, meaning
thatdisclosureofagendermedicalarmationhistorycouldbecometheunnecessaryfocus
of a health care consultation, directing attention away from the clinical issue for which care is
being sought, such as a broken arm. Similar stories were shared about those with any kind of
record of injecting drug use, or association with it – such as a diagnosis of hepatitis C – which
would often lead to a focus in a clinical consultation on drug use or opiate substitution rather
than the immediate health concern. Inadvertent or unwanted disclosure or sharing of personal
information could also lead to explicit experiences of prejudice and judgement, and to refusal of
service, based upon ignorance, fear or confusion on the part of the health care provider.
For people with HIV, people who use illicit drugs, and sex workers, the threat of criminalisation
(e.g. criminalisation of HIV non-disclosure, drug use, and sex work, which vary jurisdictionally
in Australia) meant that digital health was viewed as particularly dangerous. This in turn meant
that many key informants did not think community members should trust digital health systems
untiltherewassignicantlawreformachievedinordertodecriminalisethesepractices.
Keyinformantspointedoutthatthethreatofcriminalisationwasampliedformigrants
and refugees, as it could have implications for safety in their home country or their ability to
continue to reside in Australia. The experiences of gay and bisexual men were discussed less
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directly by key informants, and if they were, it was mainly in reference to the broader LGBTQ+
community, and in comparison to trans and gender diverse people, or in relation to people with
HIV. Key informants emphasised that given the particular histories of stigma and discrimination
in these communities, trust was mainly cultivated through particular relationships, such as
good experiences of services and health care workers being consistent, reliable, and providing
good care. This reliance on relationships with people and physical services is at odds with the
facelessness of digital health systems.
Unwanted disclosure of personal history:
If you do pursue a physical transition, you can still have health records that are in your
oldgender,thatareoatingaround.[It]becomesreallyproblematicif,forexample,
you’re admitted into emergency and you’re unconscious, and somebody pulls up your
Medicare for whatever reasons. And then everybody gets really confused and starts
focusingonthatasopposedtoanythingelse[P10]
Criminalised and stigmatised behaviours:
If you’re disclosing something that relates to criminal or otherwise stigmatised
activity and you have children, people are genuinely worried about that being reported
to child services, and maybe being reported to law enforcement. Like it happens now
because health professionals do make such referrals. I guess it’s just the unknown of
whoelsemayhaveaccesstothisinformationandthey[may]exercisetheirdiscretion
to take action against me. I really think that there needs to be like law reform, like
decriminalised sex work, decriminalised drug use at the very least, and possibly
personalpossessionofsmalleramounts[ofdrugs].[P07]
Limited evidence that health care workers deserve to be trusted:
I don’t think there’s any such thing as full trust between patients and doctors. There
are functional levels of trust. But that usually is only one-to-one and I think the
fear is when you put your trust in someone and they pass the records on or pass
informationonaboutyoucunninglyorwittinglytosomeoneelse[…][But]doweowe
our healthcare providers 100 per cent transparent insight into us, our life system? Do
wereallyowethemthatforthemtodotheirjob?[…]Imightbeadedicatedheroin
and meth user for 20 years and 10 years later not have done it for 10 years. There’s
somanyreasonsthat[thatinformation]isnotrelevanttogoingtoadoctor.Butthere
is zero chance you could walk in to see a new GP or a new doctor, for them to see
that on your record, like being in and out of rehab or whatever it is for 20 years, and
thennottobe,nottoframeyouwiththat[…]Now,itmightbeokay,theymightbe
great. They might go, “Well, who cares anyway? Even if you were still doing that, you
deservehealthcare”[…]Butit’sabitofalottery.Icannotthinkofanycircumstancein
mylife[orin]thelivesofanyoneIknowthatI’vetalkedtoaboutthis,wherebeing100
percenthonestaboutmydruguse,mycurrentdruguse,[witha]healthcareprovider
[wherethat]hasbeenapositivething.[So]youalmostinevitablyneedtolieabout
exactlywhatyoudo,becauseit’snotworththenegative[impacts][…]Formostofus,
beinghonestisnotthe,itdoesn’tpaydividendsthatweneed[P09]
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Mechanisms and conditions to establish trust in digital
health
Keyinformantssuggestedthatsignicantreformsneedtooccurinordertoestablishtrust
in digital health for Australian health care users, and particularly priority populations. These
reformsincludedpracticalmeasures,andspecicconditionsandvalues.Forexample,key
informants stressed the need for institutions to improve the transparency of how, when and
where data was collected and accessed, and the purposes for which it would be used. They
alsosuggestedthatmechanismsforprivacyanddeidenticationshouldbebuiltintodigital
healthsystems,andthatpeopleshouldhavecontroloverspecicusesoftheirdata.However,
trust was also understood to be shaped by political and social conditions which required a
more radical rethinking of the place of digital health in a modern democracy. In addition, for
priority populations who have the potential to experience criminalisation (e.g. as a result of
non-disclosure of HIV to sexual partners, illicit drug use, or sex work), law reform was viewed as
absolutely essential, before trust could be cultivated.
Some key informants wanted digital health systems to be built in ways that were inclusive of
trans and gender diverse people. Key informants noted that the inability to see oneself in a
system (e.g. systems that only used binary ‘male or female’ options for gender) suggested to
trans people that they should be sceptical about the way their data would be used, and that it
was unlikely that health care systems would recognise or understand their experiences, given
that they were not properly recorded in those systems. When gender was properly recorded in
digital health systems, some key informants were concerned about who would be permitted to
act as custodians of that potentially sensitive information.
SimilartothendingsofasocialresearchprojectconductedwithAustralianwomen(Lupton,
2019), key informants were particularly critical of the way that the Australian Government had
handled My Health Record, and felt that until the Government’s track record in implementing
digital health systems was improved, trust was unlikely to be extended by many of the
communities they worked with. Key informants wanted to see meaningful community
consultation occur with priority populations when developing and implementing digital health
systems, and were critical of consultations by institutions responsible for digital health that
demonstrated those institutions were unwilling to incorporate feedback. Some key informants
suggested that community organisations could broker meaningful engagement with
marginalised communities, including education and support regarding the promise and the risks
of participating in digital health environments.
Values and mechanisms that need to be embedded to ensure trust
in digital health:
It’s important to make the distinction between the collection of that data and also the
informed and appropriate analysis of that data. That’s where trust comes into it. How
that data is used, whether there are adequate parameters around ensuring that our
righttoprivacyisembeddedandcondentialityisrespected.[P06]
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Improving security and control mechanisms, given variable trust
in political systems:
If you had a system that was secure, the information was only to be used for the
purposes that the system was developed, if people had control over that information,
as in if they could log in and do whatever they wanted to that information, I think it
would work. It would work brilliantly. I’m not sure if we’re ever gonna be in a political
system that people will trust, but I certainly think, ideally, it will work. It would mean
that we now have enough information that people and doctors can make really
considered, positive decisions about peoples’ health, to give them the best health
carethattheydeserve.[P10]
The government needs to improve its track record in digital
health to foster trust:
Ithinkwecomebacktothislackoffaith.Lackoftrust.Unless[theGovernment]
improve their track record and give us some reason to trust them on these matters,
we’re,likealargeproportionofpeoplearenotgoingtodownloadanapp[COVIDSafe]
oroptintoMyHealthRecord.[P14]
When consumer engagement is not meaningful:
I think consumer engagement can’t be tokenistic. You know, we have a number of
examples of where consumers or through the consultation process consumers are
heavily engaged, they’re given really strong feedback, and then things have gone in a
complete opposite direction despite all the feedback from consumers. So that kind
of raises concerns around, you know, what was the point in engaging consumers if
you’renotgonnalistentothem?Andthen[they’re]havingbadoutcomesbecause
theydidn’ttakeonthatadvice.[P02]
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Findings: Community survey
Participant characteristics and internet access
A national online community survey was developed for this study, hosted on the UNSW
Qualtrics platform (Version 04/20) and conducted during April–June 2020. The survey took
approximately 15 minutes to complete. People were eligible to take part if they were adults
(aged 18 or over) and lived in Australia. Participants were provided with study information and
indicated their consent to participate at the start of the survey. Further information about the
community survey methods can be found in Appendix B.
Participant characteristics
In total, 2,914 people started the survey. Duplicate responses, responses that were completed
in less than one-third of the average time taken to complete the survey, and responses with
excessiveat-lining(i.e.answeringthesamewayonmultiplequestions)werechecked
forqualityandremovedifdeemedproblematic.Thenalsampleincluded2,240eligible
participants (76.9% completion rate). Two-thirds (n=1,463) of the sample were recruited using
a market research company that maintains a list of potential research participants (Qualtrics
Research Services). The remaining participants (n=777) were recruited via Facebook advertising
and social media networks (approximately 60% from these two sources) and promotion by our
partner community organisations.
Survey participants were recruited from every state and territory. Recruitment quotas were used
to achieve a nationally representative sample in terms of state/territory of residence based
on Australian Bureau of Statistics (ABS) data (Australian Bureau of Statistics, 2020a). Nearly
two-thirds of participants (61.0%) lived in the capital city of their state or territory. Most were
borninAustralia(75.2%)and2.9%identiedasAboriginaland/orTorresStraitIslander,making
the sample closely representative of the Australian population (Australian Bureau of Statistics,
2020b;AustralianInstituteofHealthandWelfare,2019).
Participants ranged in age from 18–87, with a median of 40 years (interquartile range 29–55).
Recruitment quotas and study advertisements aimed to recruit a higher proportion of younger
people compared to the Australian population based on ABS data. This resulted in fewer
participants in the 60 years and older age bracket, more participants in the 30–44 and 45–59
age brackets, and similar proportions of participants in the 18–29 age bracket compared to
representative population data (Australian Bureau of Statistics, 2020a).
About half of the participants (52.8%) described their gender as female (n=1182), 43.3% as male
(n=969), 2.5% as non-binary (n=56), and 1.5% used a different term to describe their gender
(n=33). Responses indicated that 2,092 participants were cisgender and 130 participants were
trans or gender diverse. Eighteen participants did not provide information about their gender
and/or their gender at birth and did not identify as trans or gender diverse. Of trans and gender
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diverse participants, 16.1% were trans men (n=21), 26.9% were trans women (n=35), 43.1% were
non-binary (n=56), 13.1% used a different term to describe their gender (n=17), and one person
preferred not to indicate their gender.
Mostparticipants(72.4%)intheoverallsampleidentiedasheterosexual.One-third(33.7%)
of participants reported being employed full-time. Nearly half the participants (47.1%) reported
havingauniversitydegree.Afthhad(22.2%)hadcaringresponsibilitiesforchildrenaged18or
younger.
Full demographic details can be found in Appendix C.
Priority populations
We relied on the following survey measures to determine if survey respondents should be
assigned membership of one or more priority populations:
• Demographic items
• Current gender and gender assigned at birth to identify trans and gender diverse
participants
• Sexuality to identify gay and bisexual men
• Aboriginal and/or Torres Strait Islander heritage to identify Aboriginal and Torres
Strait Islander people
• Health and wellbeing items
• Receiving treatment or monitoring for hepatitis B to identify people with hepatitis B
• Receiving treatment or monitoring for hepatitis C to identify people with hepatitis C
• Receiving treatment or monitoring for HIV to identify people with HIV
• Prioritypopulationchecklist,inwhichparticipantswereaskediftheyidentiedasanyof
the following categories:
• Aboriginal and/or Torres Strait Islander
• Gay or bisexual man
• Person with hepatitis B
• Person with hepatitis C
• Person with HIV
• Person who injects drugs
• Sex worker
• Trans or gender diverse person
Basedontheirresponsestothesemeasures,600participants(26.8%)wereclassiedas
members of one or more priority population group, of whom 424 (18.9%) were members of
one group, 146 (6.5%) were members of two groups, 24 (1.1%) were members of three groups,
and six (0.3%) were members of four groups. The remaining 1,640 participants who were not
identiedasmembersofanyprioritypopulationgroupwereclassiedas‘generalpopulation’
participants. Unique and multiple memberships across priority populations are shown in Table
C2.
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FrequenciesandproportionsforallmeasuresarereportedintheAppendices,stratiedby
general population and priority population groups. Because some participants were members
of multiple priority population groups, statistical comparisons at the bivariate level between
priority population groups were not possible. The reported frequencies and proportions should
therefore be interpreted with reference to the selected multivariate analyses provided in this
reportwhichidentiedindependentassociationsbetweenprioritypopulationgroupmembership
and the outcomes of interest, as described under Statistical Analyses in Appendix B.
As discussed in the Methods, owing to relatively small group sizes, results for Aboriginal and/
or Torres Strait Islander people, people with hepatitis B, people with hepatitis C, and people who
inject drugs are not provided separately, but are still included in the total sample.
In the rest of this results section, any reference to ‘priority populations’ should be taken to mean
all of those that were included in the multivariate models, i.e. gay and bisexual men, people with
HIV, sex workers, and trans and gender diverse people.
Internet access
Participants were asked to rate their access to the internet in terms of ease and speed. Most
responded that they could access it whenever they needed it (60.9%) or most of the time
(32.2%). Most participants accessed the internet using a smartphone (87.2%) or a computer
(88.0%). Results were similar for the general population and priority population groups. Table
C3 shows quality of internet access and the range of devices used by participants to access the
internet, by priority population group.
Health and wellbeing
The majority of participants (93.7%) had access to government subsidised health care through
Medicare, of whom 823 (39.2%) had a Health Care Card for further subsidy of medicines and
health care costs (see Table D1). Access to government subsidised health care was similar
across the general population and priority population groups.
Overall, priority population groups reported more complex health needs than the general
population. Priority populations were more likely to report having one or more long-term
health conditions (see Figure D1), with people with HIV being the most likely to report multiple
conditions. Trans and gender diverse people and people with HIV were more likely to rate their
health as poor or fair (see Figure D2). Priority populations were more likely to report receiving
mental health care, with a much greater likelihood among trans and gender diverse people,
followed by sex workers, gay and bisexual men, and people with HIV (see Figure D3). These
three measures of health (having one or more health conditions, self-rated quality of health, and
receiving mental health care) are included as covariates in selected analyses.
Priority populations were more likely to report taking greater numbers of prescription and over-
the-counter medications compared to the general population. People with one or more long-
term health conditions, people who had poor/fair health (self-rated), people who were receiving
mental health care, trans and gender diverse people, sex workers, and people with HIV were
more likely to take four or more prescription medications (see Figure D4).
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The majority of participants (88.8%) had attended a general practice during the past year.
Priority population members were more likely to have attended a pharmacy (~80–90%) in the
past year compared to nearly two-thirds (66.1%) of general population participants. Further
details about health status and attendance at health care services are shown Table D2.
Trans and gender diverse people, people with HIV, sex workers and people living in regional
areas were less likely to report having good or excellent access to health care based on
affordability or the location of services (see Figure D5).
Full details about the health and wellbeing of participants can be found in Appendix D.
Stigma indicators
Participants were asked to rate how frequently they had experienced stigma or discrimination
during the past 12 months with relevance to their priority population membership: e.g. sex
workers were asked if they had been treated differently because they were a sex worker, people
livingwithHIVwereaskediftheyhadbeentreateddifferentlyduetotheirHIVstatus.Therst
itemrelatedtogeneralstigma(Broadyetal.,2018).Theseconditemaskedspecicallyabout
stigma experienced within health care settings. Responses were scored on a Likert-type scale
from (1) ‘Never’ to (5) ‘Always’. Stigma questions were shown to participants based on their
responses to demographic items and the priority population question. For example, sexual
orientation was shown to all participants who did not select heterosexual in the demographics
section as well as participants who selected ‘gay or bisexual man’ from the priority population
list.
Looking at the whole sample, frequent experiences of stigma (ratings of ‘Often/Always’)
were most common among sex workers (57%), followed by trans and gender diverse people
(32%), people with HIV (8%), and people who did not identify as heterosexual (7%). Frequent
experiences of stigma were reported by 7% of gay and bisexual men (excluding other non-
heterosexual participants).
Frequent experiences of stigma in health care settings were most commonly reported by sex
workers (29%), followed by trans and gender diverse people (18%), people with HIV (5%), and
people who did not identify as heterosexual (4%). Frequent experiences of stigma in health
care settings were reported by 4% of gay and bisexual men (excluding other non-heterosexual
participants).
Further information about participants’ experiences of stigma can be found in Table D3.
Trust
Generalised trust
Generalised trust in other people (a measure derived from the social capital literature)
was measured using a version of the ‘Most people can be trusted’ scale (Lundmark et al.,
2016). Responses were scored from (1) ‘You can’t be too careful’ to (5) ‘Most people can be
trusted.Ratingsoffourormorewereclassiedasgenerallytrusting.One-third(33.7%)ofthe
participantswereclassiedasgenerallytrusting,withlowerlevelsoftrustobservedamongsex
workers, trans and gender diverse people, and people with HIV. People with a university degree
were more likely to be trusting, whereas people who lived in regional areas, people who had
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poor/fair health, people with HIV, and sex workers were less likely to be trusting of other people
(see Figure E1). Aggregated data for this measure are shown in Table E1.
Trust in digital technologies that use personal information
Participants were asked to rate their level of trust in digital technologies which use their
personal information, for example, their full name, date of birth, address and credit card details.
Responses were scored on a Likert-type scale from (1) ‘Distrust a great deal’ to (5) ‘Trust a great
deal’.Trustwasdenedasscoresoffourormoreonthescale(i.e.,Trust/Trustagreatdeal).
Just over a quarter of participants (27.1%) indicated they were trusting of digital technologies
that used personal information. Scores were generally lower than generalised trust scores but
followed a similar pattern, with the lowest scores expressed by sex workers (13.7%), trans and
gender diverse people (18.5%), and people with HIV (19.6%). Aggregated data for this measure
are shown in Table E1.
Trust in institutions
Participants were asked to rate their level of trust in a range of institutions and organisations
(see Table E1). Responses were scored on a Likert-type scale from (1) ‘Distrust a great deal’
to(5)‘Trustagreatdeal’.Scoresoffourormoreonthescalewereclassiedastrusting(i.e.,
Trust/Trust a great deal).
In the whole sample, the most trusted people and institutions were scientists (65.4%) and
universities (47.2%). The least trusted institutions or organisations were commercial media
(12.3%) and major international companies (14.7%). Priority population groups were generally
less trusting than the general population, except for trust in the environmental movement, non-
commercial media and scientists, which was higher among priority populations.
Trust in health care services to keep information private and
confidential
Participants rated their level of trust in health care services to keep their information private
andcondential(seeTableE2).ResponseswerescoredonaLikert-typescalefrom(1)‘Distrust
agreatdeal’to(5)‘Trustagreatdeal’.Scoresoffourormoreonthescalewereclassiedas
trusting. Participants were only asked about the health care services that they had attended in
the past year.
Across the whole sample, most people (70–80%) trusted their health care services to keep their
informationprivateandcondential.Themosttrustedhealthcareserviceswerementalhealth
care and general practice services.
Of the priority populations, sex workers were generally less likely to trust GPs, pharmacies,
dentists, in-patient hospitals, and out-patient hospitals or clinics to keep their information
privateandcondential(seeFiguresE2–E6).Transandgenderdiversepeoplewerelesslikely
to trust pharmacies, in-patient hospitals, and out-patient hospitals or clinics to keep their
informationprivateandcondential.OthersignicantassociationsareshowninFiguresE2–E6.
Gay and bisexual men and people with HIV generally had similar levels of trust in health care
services to the general population.
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 20
My Health Record
Access to and knowledge about My Health Record
Nearly a quarter of participants (23.3%) had an Australian electronic health record (My Health
Record) and had accessed it at least once, while another quarter (25.8%) had a record but had
never accessed it. More than a quarter of participants (29.4%) had opted out of or deleted their
record. The remaining participants did not know if they had a record (18.0%) or were not eligible
for one (3.5%). See Table F1.
Priority populations were much more likely to have opted out of My Health Record than the
general population sample. People with a university degree and people with one or more long-
term health conditions were also more likely to have opted out, while people who had poor/fair
health (self-rated) were less likely to have opted out (see Figure F1).
Less than a third of participants (28.8%) reported knowing ‘quite a bit’ or ‘a lot’ about My Health
Record. People with a university degree, people with one or more long-term health conditions,
and trans and gender diverse people were more likely to know more about My Health Record,
while people who had poor/fair health were less likely to know about it (see Figure F2).
Common sources from which participants learned about My Health Record were media articles
including social media (49.0%), government advertising campaigns (44.2%) and conversations
with friends and family (30.2%). Overall, a relatively small proportion of participants (15.7%)
hadlearnedaboutMyHealthRecordfrominformationprovidedbycommunityorganisations;
however, sex workers, people with HIV, trans and gender diverse people, and people with a
university degree were more likely to have learned about it via this source (see Figure F3).
Further information about participants’ access to and knowledge about My Health Record is
shown in Table F1.
Use of My Health Record among participants who had a record
Among the 1,100 participants who had a My Health Record, frequency of use was relatively
low. Small numbers of participants had accessed their record weekly (0.3%) or monthly (5.2%)
during the past year. Over a third (34.4%) had accessed their record once or twice during the
past year. Just over half (52.5%) of all participants who had a My Health Record had never
accessed it, while a further 7.7% had accessed their record more than a year ago.
The most common reasons for accessing My Health Record were to know what was in the
record (46.7%), followed by keeping informed (20.8%) and making sure the record was accurate
(18.9%). Among participants who had accessed their record, the most common interactions on
the platform were adding personal notes (28.5%), choosing what content could be seen (24.3%),
settingaccessnoticationstoshowwhensomeonehadviewedorchangedtherecord(18.0%)
and setting health care service access restrictions (17.2%).
Among participants who had a My Health Record, participants indicated that the most useful
information in the record was test results (66.2%), medication history (58.7%), immunisation
history (57.2%) and doctors’ notes (55.1%).
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More than half of participants (54.0%) were unsure if their doctor had accessed or used the
participant’s My Health Record. Nearly a quarter (24.7%) reported that their doctor(s) had added
informationtotheirrecordandnearlyafth(19.8%)reportedthattheirdoctor(s)hadaccessed
information from the record.
Relatively small numbers of priority population groups had a My Health Record and had
accessed it, so further analyses of associations between priority population membership and
different types of use of My Health Record were judged to be unreliable.
Further details about the use of My Health Record and potential concerns about it are shown in
Table F2.
Reasons for opting out of My Health Record
Reasons for opting out of or deleting My Health Record are shown in Table F3. Nearly all
participants who had opted out of My Health Record indicated they had concerns about it. The
most common reason was concern that the government could not adequately protect their
privacy (79.3%), followed by concerns about data being shared between government agencies
without consent (67.3%) and medical information being hacked or leaked (67.2%).
Priority populations were more likely to endorse a range of concerns relating to data privacy,
data being shared without their consent, and concerns about being treated disadvantageously
by the government and health care professionals. In particular, sex workers were much
more likely to report opting out of or deleting My Health Record due to concern about health
professionals not treating them with dignity and care, which was a concern shared by trans
and gender diverse people and people with HIV (see Figure F4). Sex workers were also much
more likely to report opting out due to concern about data being shared between government
agencies without their consent and due to concern that the government might use their data in
ways that disadvantaged them. These concerns were shared by gay and bisexual men and trans
andgenderdiversepeople(seeFiguresF8andF10).Othersignicantassociationsareshownin
Figures F4–F12.
Information that might have been useful among participants who
had opted out of My Health Record
Among participants who had opted out of or deleted My Health Record, the information that
was most commonly rated as useful if they had kept a record was their immunisation history
(55.2%), test results (55.0%), medication history (50.8%) and doctors’ notes (47.0%). These
types of information were also perceived as the most useful by participants who had a record
(Table F2), which suggests that the perceived utility of the types of information stored in My
Health Record was similar regardless of the concerns that some participants had about the
platform.
Willingness to share My Health Record data with health care
services
Participants were asked how willing they would be to allow relevant information from an
electronic health record to be shared with health care services. All participants were asked
these items regardless of whether they indicated they had opted out of or deleted My Health
Record, but participants who responded that they were not eligible for a record were not asked
these items.
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All participants were shown the items in relation to GP services, pharmacies, dentists, in-patient
hospital and out-patient hospitals or specialist clinics. Participants were only asked about the
other health care services if they had attended them in the past year, e.g., community-based or
peer-led sexual health clinics, allied health services and mental health services. Responses were
scored on a Likert-type scale from (1) ‘Not at all willing’ to (5) ‘Very willing’, and willingness was
denedashavingascoreoffourormoreonthescale(i.e.,Willing/Verywilling).Allaggregated
data for these measures can be found in Table F4.
Overall, most participants (77.4%) indicated that they were willing for relevant information to be
shared with their GP, followed by in-patient hospitals (73.9%), out-patient hospitals or specialist
clinics (68.5%), dentists (56.9%) and pharmacies (56.0%). However, priority population groups
were generally less willing to share relevant information from an electronic health record with
health care services.
Specically,sexworkers,peoplewithHIVandtransandgenderdiversepeopleweregenerally
less willing to share relevant information from an electronic health record with GPs (see
Figure F13), pharmacies (see Figure F14), in-patient hospitals (see Figure F16), and out-patient
hospitals or specialist clinics (see Figure F17). Sex workers and trans and gender diverse people
were generally less willing to share relevant information from an electronic health record with
dentists (see Figure F15). In contrast, gay and bisexual men were generally more willing to share
relevant information when it came to GPs, in-patient and out-patient hospitals or specialist
clinics (Figures F13, F16–F17).
Willingness to share My Health Record data with government
agencies and industry bodies
Participants were also asked how willing they would be to allow relevant information from an
electronic health record to be shared with health-related government agencies, non-health-
related government agencies, health insurance companies, law enforcement and banking or
nancialinstitutions.AllaggregateddataforthesemeasurescanbefoundinTableF5.
Factors associated with willingness to share My Health Record data with government agencies
and industry bodies are shown in Figures F18–F22. Priority populations, people with a university
degree and one or more long-term health conditions were generally less willing to share My
Health Record data with these organisations. Of note, trans and gender diverse people and
sex workers were far less likely to support My Health Record data being shared with these
organisations.
Support for sharing de-identified My Health Record data for
research
Participantswereaskedhowmuchtheysupportedtheirde-identiedhealthinformationfrom
My Health Record being shared for non-commercial research, commercial research, research by
health-related government agencies, and research by non-health-related government agencies.
ExamplesofthetypesofinstitutionsthatwereincludedareshowninTableF6.De-identied
wasdenedashavingone’sname,address,contactdetailsandotheridentifyinginformation
removed. All participants were asked about these items regardless of whether they indicated
they had opted out of or deleted My Health Record, but participants who responded that they
were not eligible for a record were not asked these questions.
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Responses were scored on a Likert-type scale from (1) ‘Strongly oppose’ to (5) ‘Strongly
support’.Supportwasclassiedasascoreoffourormoreonthescale(i.e.,Support/Strongly
support).Acrossthewholesample,supportwashighestforsharingde-identieddatawith
health-relatedgovernmentagencies(44.7%),butsupportwasloweramongprioritypopulations;
this was followed by support for sharing data for non-commercial research by a university or
research institute (43.7%), which was generally higher among priority populations. Support for
de-identieddatabeingusedforresearchbycommercialandnon-health-relatedgovernment
agencies was generally low. All aggregated data for these measures can be found in Table F6.
Aggregated data for all My Health Record and other health data measures can be found in
Appendix F.
Digital technologies and services
Use of digital technologies to manage health
Use of digital technologies including websites, apps and other services to manage health
or share health information are shown in Tables G1–G3. The bolded values represent the
participants who had used each of the platforms. For each platform they had used, participants
were asked to rate their level of trust in that platform to manage the security and privacy of their
information. The ratings of trust are nested under each platform type. Responses were scored
on a Likert-type scale from (1) ‘Distrust a great deal’ to (5) ‘Trust a great deal’. Scores of four or
moreonthescalewereclassiedastrusting(i.e.,Trust/Trustagreatdeal).
Overall, the most used and trusted service was online or phone health consultations (25.1%,
n=562). Of those who had used this type of service, 78.2% trusted the service to manage the
security and privacy of their information. The next most used services were apps to claim
Medicare or private health insurance rebates (15.7%, n=352), trusted by 72.2% of people who
hadusedthem.Useofautomaticandmanualhealthortnessappswaslesscommon(29.1%
and 20.9% of participants, respectively), and notably, fewer than half of the participants who had
used these apps trusted them to manage the security and privacy of information (both 46.8%).
Aggregated data on the use of and trust in these technologies, including other websites, apps
and services can be found in Table G1–G3.
Willingness to share health data from a smartphone or wearable
device app with health care services
Participants were asked how willing they would be to share health data from a smartphone or
wearable device app with health care services. All participants were asked about sharing data
with GP services, pharmacies, dentists, in-patient hospital and out-patient hospitals or specialist
clinics. Participants were only asked about the other health care services if they had attended
them in the past year. Responses were scored on a Likert-type scale from (1) ‘Not at all willing’
to(5)‘Verywilling’,andwillingnesswasdenedasascoreoffourormoreonthescale(i.e.
Willing/Very willing). All aggregated data for these measures can be found in Table G5.
Across the whole sample, willingness to share data from a device or app with various services
was approximately 50–60%. Sex workers and trans and gender diverse people were far less
likely to be willing to share health data from a device or app with health care services, whereas
gay and bisexual men were more willing to share such data from a device or app with GPs
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(see Figure G1). Further detail about factors associated with willingness to share data in these
contexts is shown in Figures G1–G5.
Willingness to share health data from a smartphone or wearable
device app with government agencies and industry bodies
Participants were also asked how willing they would be to share health data from a smartphone
or wearable device app with health-related government agencies, non-health-related government
agencies,healthinsurancecompanies,lawenforcementandbankingornancialinstitutions.All
aggregated data for these measures can be found in Table G6.
Factors associated with willingness to share health data from an app with government agencies
and industry bodies are shown in Figures G6–G10. Priority populations, people with a university
degree and people with one or more long-term health conditions were generally less willing to
share health data from an app to these organisations. Of note, trans and gender diverse people,
sex workers, gay and bisexual men, and people with any long-term health condition (regardless
of HIV status) were far less likely to support data being shared to these organisations.
Aggregated data for all digital technologies and services measures can be found in Appendix G.
Novel coronavirus (COVID-19) measures
Changes in behaviour or views in response to COVID-19
Participants were asked questions about whether they had changed their health-related
behaviours or views in response to COVID-19. A small minority of participants (1.2%) indicated
they had been diagnosed with COVID-19. Most (93.0%) had been practicing physical or social
distancingtoavoidgettingorpassingonCOVID-19,oneinve(21.8%)wererequiredtoself-
isolate (or stay in quarantine), and approximately one-third (36.1%) had lost income due to
COVID-19. Sex workers were much more likely to report a loss of income than other participants
(83.5%).
A small number of participants (3.7%) reported making changes to My Health Record due to
COVID-19, the reasons for which are shown in Table H1.
Table H1 also details changes to the ways in which participants accessed health services
or information due to COVID-19. The most common changes were having a remote health
consultation (29.8%), followed by requesting a script over the phone (18.6%). These changes
were generally more commonly reported by priority populations. Priority population participants
were more likely to have had remote consultations with medical professionals, requested a
script by phone, stocked up on prescription medications, and sought out health information due
to COVID-19.
A minority of participants (16.5%) had used an online symptom tracker to monitor COVID-19
symptoms. The reasons for their use are detailed in Table H1.
Information that participants would be willing to share with health
authorities to help the response to COVID-19
Participants were asked what information they would be willing to share with health authorities
to help the response to COVID-19. Overall, the information that participants were most willing to
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share with health authorities was their recent travel history (63.2%), recent symptoms (54.9%) or
health conditions or diagnoses (49.7%).
Responses were similar regardless of priority population membership, however lower
proportions of priority populations were willing to provide personal details (e.g., age, gender,
sexuality, employment status). Less than a third of participants (32.6%) reported being willing to
share information via Bluetooth tracking, and this was much lower among sex workers (14.4%)
and trans and gender diverse people (17.7%). Further information about this measure is shown
in Table H2.
Free text responses
Three questions in the survey provided participants with an opportunity to write an open-ended
answer in their own words.
A large number of free text responses were received to these questions, providing us with a rich
set of qualitative data to complement and expand upon the information collected in quantitative
form.
We have provided a few examples of free text responses to each of the three questions
(mostly in their original form, including some spelling mistakes), as well as the number of total
responses we received for each question:
Are there any particular aspects of your personal health information or history that you feel
worried about storing and sharing in digital health systems? (687 responses)
I simply don’t disclose that I am a sex worker, or trans, or queer/asexual unless it
absolutelyneededbecauseIhavehadhorricexperienceswhendoingsopreviously.
I trust my current gp, endo, and psych so they know these things can’t guarantee that
future health workers I see will be as safe or unpredjudiced
I access healthcare primarily for sexual health and general sickness currently, and
I don’t have faith in the federal government creating IT infrastructure with the nec-
essary privacy constraints or kinks worked out just yet. Also, as some that works in
government and multiple “customer relationship management” systems, I dont trust
completely the ability of workers in government agencies and health services, despite
their best intentions, to maintain data privately or in accordance to the law. Human
error is human error. Further to this, queer health in many aspects is incredibly mor-
alised and pathologised in contemporary Australia - I dont want my data to be collect-
ed by any centralised database for whatever use.
Is there anything that would make you feel more comfortable about sharing your personal data
with health authorities, either during COVID 19, or beyond? (491 responses)
Robust ethics approval process. Sunset clauses and restrictions on the storage, use
and access of data
I understand the public health importance of this kind of tracing but I think for his-
torically targeted communities it makes sense to opt out. As a migrant sex worker, I
wouldn’t risk it, no matter how well they think they’re doing with digital security.
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The governement is constantly announcing new ways to punish poor, disabled, di-
verse Australians. I can’t trust it. Maybe 10 years of the governement honestly trying
to help poor, sick, disabled, diverse race, gender, sexuality Australians might change
my mind
Is there anything else that you would like to tell us that hasn’t been covered in the survey? If so,
please let us know below. (322 responses)
I’d be happy to share my info with my consent. If there was some type of alert that I
could set up that could allow me to consent or withdraw consent for my data being
usedforsomethingI’dbenewiththatbutnotpeoplejustusingmydatawithoutmy
knowledge.ThatappliestoallserviceswhereIhaven’tconsentedatrsttoo(egI
alwaysconsenttohospitalsusingmydatabutIwouldn’tconsenttomyFitbit[d]ata
being sold/used unless I knew exactly where and why).
It is imperative that digital health be implemented as comprehensively as possible,
BUT it MUST be incredibly secure. I want 2-factor on everything, for instance.
I have an absolute belief in the value of data for public and personal good. It is excru-
ciating to watch things that could be amazing bungled. MyHealth Record broke my
heart for it’s lost potential. Don’t let the potential value of a contact tracing app suffer
the same fate.
Analysis of the free text data will be conducted for publication in journal articles.
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Discussion
Thekeyndingsdescribedinthisbriefreportconrmthattrustisacriticalissueaffecting
engagement with digital health systems and technologies in Australia. In particular, results
demonstrate that some of the populations affected by BBV/STIs report lower levels of trust in
many aspects of the health care system, including digital health, despite also reporting high
levelsofengagementwithandunderstandingofthepromiseandbenetsoftheseinnovations.
Thiscontrastsuggeststhattrustindigitalhealthinthiscontextisinuencedlessbythe
technical design or digital literacy issues associated with consumer engagement, and more
by the relational and structural factors which underpin trust in the institutions responsible for
health system design and regulation.
Keyinformantsemphasisedthemanybenetsthatdigitalhealthsystemscouldprovideto
communities marginalised by mainstream systems, but also outlined many examples of those
whose personal health information puts them at risk of stigmatisation and criminalisation if
data security and privacy are not well protected. Indeed, while there were distinctive needs and
concernsidentiedamongdifferentcommunities,keyinformantsarguedthattrustindigital
health was commonly affected by the pervasive and persistent stigma and discrimination
experienced in health care settings, the criminalisation of particular behaviours related to HIV,
sex work, and drug use, and the potential for personal information, or health information related
to stigmatised or pathologised identities or practices, to be shared without the knowledge or
consent of the affected person.
Meaningful consultation, law reform, inclusive system design, and mechanisms for community
members to control data access were proposed by key informants as essential for increasing
trust. Thus, in addition to driving new technological innovations, resources should be directed
towards remediating the social, cultural, and political issues that continue to marginalise
somecommunitiesfromparticipatingindigitalhealthsystems.Thisndingunderlinesthe
importance of recognising that the legal and policy context shapes the conditions within which
choices are made about digital health. Results also provide additional evidence to support
the ongoing resourcing of, and commitment to, efforts to ‘monitor laws, policies, stigma and
discrimination which impact on health-seeking behaviour among priority populations and their
accesstotestingandservices;andworktoamelioratelegal,regulatoryandpolicybarrierstoan
appropriate and evidence-based response’ which feature in the HIV and STI national strategies
(Australian Department of Health, 2018a, p. 32, 2018b, p. 32). Peer support and advocacy
organisations play an essential role in both monitoring and driving change in the legal and policy
context that impacts the capacity of these communities to engage with digital health.
The community survey provides important insights into the variety of ways in which the
Australian community in general, and particularly the key priority populations that we reached,
feel about sharing their personal health information with a variety of institutions and services
and through a range of technologies and platforms. The results show that communities who are
marginalised by prejudice and the risk of criminalisation generally report less trust across most
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of the measures we included, even though as communities with high levels of health service
engagement they potentially also have much to gain from digital health. This has particular
implications for communities who are currently less well represented in existing data collection
practices because of a lack of willingness to adapt service intake forms and population surveys,
as just two examples, to meaningfully and appropriately capture information on sexuality,
gender and intersex traits. Given it may be harder to collect personal information among groups
who may fear that data will be shared or misused, stronger processes for securing consent and
protecting personal data must accompany these forms of data collection, including sensitive
and stigmatised information relating to drug use or sex work. Indeed, consent for collection and
use of data was a major theme across both the survey and key informant data, but particularly
so for sex workers and trans and gender diverse people. Finding new and more effective ways
to ensure that consent is secured to collect, store and share health data, and that consent is
specic,dynamic,andinformed,willbeessentialtoaddressingtheseconcerns.
Priority populations were also more likely to express concerns about being treated poorly by
both the government and by health care professionals. It is important to note that those groups
who reported concerns about how they might be treated also reported more frequent previous
experiences of stigma and discrimination. This is consistent with research that has found past
experiencesandfutureexpectationsofdiscriminationandstigmaaresignicantbarriersto
healthcareaccessandtreatmentadherence(Hatzenbuehleretal.,2013;Kerretal.,2020;Turan
etal.,2017;vanBoekeletal.,2013).Inaddition,thosegroupswhoreportedthemostpersistent
forms of prejudice and stigma faced extensive work ahead in addressing their rights and
standing in the Australian community (Davis & Manderson, 2014). This is woven into the issues
underpinning trust in digital health. In the survey, for example, sex workers consistently reported
less trust and more experiences of stigma. In order for conditions to be created in which greater
trust is felt by priority populations, major investments are required in discrimination and stigma
reduction strategies at every level: workforce training, service redesign, community education
and resilience building, and in some contexts, law reform.
Across the priority population participants, there appeared to be a common pattern of being
highly engaged with health care, but also being reluctant to trust digital health systems with
personal data. For example, all four priority populations reported higher recent use of online
consultations and online pharmacies than the general population respondents. They were more
likely to report gathering essential medications and organising online health consultations in the
early weeks of the COVID-19 pandemic. While this level of engagement with health services may
be explained by a higher prevalence of chronic conditions or other reasons for seeking care,
this does not explain why they were also more reluctant to engage with some aspects of digital
health,despiteknowingalotaboutitspotentialbenets.Forexample,allfourgroupsreported
better knowledge of My Health Record than the general population, and a greater willingness
tosharede-identiedhealthdatawithnon-commercialresearchers.Andyet,thesegroups
were much more likely to report that they had opted out of My Health Record. The perceived
benetsofhavingaMyHealthRecordweresimilaramongthosewhohadoptedoutandthose
whohadarecord.ThissuggeststhatanunderstandingofthepotentialbenetsofMyHealth
Record did not overcome the doubts that individuals considered when opting out. The four
priority population groups generally reported lower rates of willingness to share personal data
with health care services of any kind, and with government and commercial agencies. This
paints a picture of these communities as highly engaged, well informed, and notably reluctant,
to put their trust in some aspects of digital health. They appear to opt out of initiatives like My
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Health Record not because they do not understand the promise offered by more integrated
and effective data management systems, nor because they do not have need of these
improvements,butbecausetheyarenotconvincedthepotentialbenetsoutweightheriskto
their personal privacy and security, at least in the form in which they have been designed and
promoted. This opting out may be understood as ‘informed refusal’: an action which reminds us
that consent to interventions should not be expected, and the decision of priority populations to
refuse participation, to opt-out, is a necessary condition of justice (Benjamin, 2016).
There was some discussion in the key informant interviews, and some evidence to support this
in the community survey, that priority populations may be more willing to trust in relationships
which are longstanding and consistent, such as with a trusted clinician, but be less willing to
trust in government agencies and systems which do not have the same opportunities for direct,
interpersonal relationships to be built and maintained over time. Peer-based organisations can
therefore be seen to play an important role in mediating between communities and government
systems and could play a stronger and formalised role in supporting community engagement
with digital health systems. In addition to resourcing these organisations to facilitate dialogue
between governments, industries and communities, there is potential to explore the role of
peer- and community-based organisations as data brokers, navigators, and custodians, ensuring
that data is managed, and systems designed, in ways that are grounded in and responsive to
community needs and concerns. There is also much room for improvement in increasing the
trust these communities feel in particular health services. For example, priority populations
reported the lowest levels of trust in sharing data with dental and pharmacy workforces. While
thiscouldreectaperceptionthattheseserviceshavelessneedtoaccesspersonalhealth
data,itmayalsoreectaneedforpeer-basedorganisationstohaveanexpandedrolein
educating and assisting health services and other workforces about best practice in collecting
and storing sensitive health information. This may include education about respectful and
inclusive practices and ways to ensure that only sensitive information that is relevant and
necessary for the delivery of care is collected and stored. These measures may facilitate
increased trust among marginalised and stigmatised communities. Regulatory mechanisms are
likely also required to improve the perceived trustworthiness of these and other services.
There were some differences observed between priority populations. Gay and bisexual men, for
example, were generally quite similar to the general population in terms of how willing they were
to trust health care services with their health information, and were far more willing to share
their personal data via an app with their GPs, compared with the other priority populations.
There are a few possible explanations for this. Greater proportions of gay and bisexual men in
the sample resided in NSW and Victoria, compared to the overall sample, and two-thirds of the
gay and bisexual men in these two states resided in Sydney and Melbourne. It is possible that
many of these respondents are able to attend clinics with specialist expertise in health care
for gay and bisexual men, noting that there are a number of LGBTQ+-friendly health services in
Sydney and Melbourne (Newman et al., 2013), and gay and bisexual men living in regional areas
continuetoreporttravellinglongdistancestoattendapreferredhealthservice(Holtetal.,2010;
Lea et al., 2019). Frequency of service use may also lead to greater levels of trust, but if that was
the case here, we would have expected to see similar levels of trust in other priority populations
who frequently accessed services. However, it is also possible that gay and bisexual men avoid
disclosing their sexual practices in health care settings in which they do not feel it is safe to do
so, particularly but not only those men who also have relationships with women (Newman et al.,
2018). Non-disclosure may be less of an option for the other priority populations, who are often
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 30
required to be very open about the information they are concerned about sharing in order to
receivetheservicestheyneed,suchasgenderarminghormones,HIVtreatmentandcare,and
sexual health screening (in jurisdictions which have mandatory testing arrangements in place
for the regulation of sex work, for example).
Itisalsopossiblethatthesignicantimprovementsingeneralpublicattitudestowards
homosexuality (de Visser et al., 2014), most notably represented by marriage equality, achieved
in December 2017, have led to gay and bisexual men (particularly cisgender gay men) no longer
experiencing the intensity of the homophobic attitudes which were once commonly expressed
inhealthservicesandinthewidercommunity,especiallyduringtherstdecadesoftheHIV/
AIDS epidemic. This positive development suggests that the other priority populations, who do
notyetenjoythislevelofsocialsupport,wouldbenetfromgreaterengagementinactivism,
decriminalisation and social movements which have improved the rights and standing of sexual
minoritiesinAustralia,particularlysincetheHIV/AIDSepidemic(Power,2011;Reynolds,2002;
Sendziuk, 2003).
As noted in the limitations (Appendix I), this research was conducted in the early weeks of
the COVID-19 pandemic. We were able to capture additional data pertaining to this context,
although much of this context continued to change quite rapidly through the period of data
collection and analysis. Notable insights for the COVID-19 response was that most of the
community survey sample was willing to share their personal information with health authorities
forthepurposeofpandemicmanagement,butourndingssuggestthatsomeofthepriority
populationsweremuchlesslikelytofeelthatthiswasajustiedriskoftheirprivacy.This
speakstotheurgentneedtoremovetheidentiedbarrierstoengagementwithdigitalhealth
systems for priority populations, including prejudice, stigmatisation and criminalisation, as
necessary for supporting these groups in the pandemic response. This is a timely example of
how committing resources to improving the conditions which are required for trust to be rebuilt
among the most marginalised groups in Australia is a critical component of maintaining the
health and wellbeing of the whole community.
Research is still needed to explore issues related to trust in digital health for the priority
populations we were not able to target in this exploratory study, including people who use
injecting and illicit drugs, Aboriginal and Torres Strait Islander people, and people with viral
hepatitis. Research with these groups would ideally be conducted through methods other
than online general population recruitment, to ensure that these communities could be
effectively reached and engaged. Rich insights could be achieved by taking an intersectional
approach to designing and analysing research on this topic, given the distinct experiences of
individuals who belong to more than one of these populations. With origins in African-American
feminism(Crenshaw,2019),intersectionalitypermitsmeaningfulunderstandingofthespecic
experiences of those who lives are impacted by more than one system of oppression. This
means, as just one example, that the experiences of sex workers of colour must be recognised
as distinctive from the experiences of sex workers as broader group. The experiences of priority
populations who sit at the intersections of these imposed categories have much to add to
what is known about the conditions underpinning trust in digital health systems. As the context
continues to change, particularly but not only in response to the COVID-19 pandemic, new
research will also be needed to better understand how these communities are making sense of
the options, opportunities and threats involved in digital health. Finally, there is much to learn
from those working to achieve data justice (Dencik et al., 2019), including feminist calls to value
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 31
multipleformsofknowledge,beyondthequantiable(D’Ignazio&F.Klein,2020).Forexample,
there is a renewed push towards achieving ‘HIV data justice’ in the context of the molecular
surveillance of HIV (Molldrem & Smith, 2020), which aims to abolish public health logics that
objectify people with HIV in favour of ‘bio-informational self-determination’ (Bernard et al.,
2020).Attentionmustalsobegiventobetterunderstandinghowdigitaltechnologybothreies
and produces new forms of racialised oppression (Benjamin, 2019). Indigenous perspectives
on data governance and ‘data sovereignty’ (Carroll et al., 2019) also offer approaches to
rethinking the management of personal data, prioritising Indigenous perspectives on the kinds
of information that is valued and the ways in which that information should be collected and
used (Walter & Suina, 2019). These practices and principles have much to offer a re-imagined
approach to digital health data governance for the communities who participated in our study.
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 32
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Wong, C. M. L., & Jensen, O. (2020). The paradox of trust: Perceived risk and public compliance
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UNSW Centre for Social Research in Health
Understanding trust in digital health among communities affected by BBVs and STIs in Australia 39
Appendices
Appendix A: Key informant interview methods
Individual, semi-structured interviews were conducted with purposively selected key informants
during March–June 2020, including advocates, policymakers, researchers, and clinicians.
Participantswereidentiedthroughformalandinformalnetworksandwereeligibletotake
part if they held professional knowledge on digital health and/or blood-borne virus/sexually
transmissible infections policy and practice, and were aged 18 or over, resident in Australia, and
procientinEnglish.Interviewslasted20–67 minutes (average 48 minutes).
Theinterviewguideforthekeyinformantinterviewswasdevelopedspecicallyforthestudy.
Theinterviewguideexploredhowparticipantsdenedtrustanddigitalhealth,andtheirviews
on how the communities they worked with felt about and engaged with digital health systems.
Theguidealsoexploredthepromise/benetsaswellasrisks/consequencesofdigitalhealthfor
these communities, any differences in these views and practices based on the particular digital
environment,andmechanismsorinitiativesthatmightmaximisethebenetsandminimisethe
risks, and/or help to rebuild the trust of these communities in digital health systems.
Key informants were invited via email, and interviews were all conducted remotely, by either
phone or Zoom. Audio recordings were transcribed by a professional transcriber and checked
againsttheoriginalrecordingsforaccuracy.Transcriptswerethende-identied,toensurethat
any identifying information, such as names, places, and organisational roles were removed.
Interview data were analysed using both a deductive and inductive approach to thematic
analysis (Braun & Clarke, 2006). Analysis was discussed amongst team members to ensure
sucientvariationanddepthwasachieved,includinginananalysisworkshop,andthenin
written manuscript form.
Although the key informant sample spanned a diverse range of perspectives (see Table
A1), there was a high proportion of participants representing peer-based non-government
organisations, and drawing on expertise in advocacy, education and policy. There was
particular expertise in the experiences and needs of trans and gender people, people with
HIV, sex workers, and gay and bisexual men, but we had representation from all of the priority
populations in the national BBV and STI strategies, including people who inject drugs, young
people and Aboriginal and Torres Strait Islander people. The sample as a whole was quite
young for a professional interview study, with a majority born in the 1980s, and a relatively high
number of participants identifying as queer, gay, and bisexual/pansexual. Almost all participants
identiedwithaWhiteorAnglo/Irishheritage,andalmostallparticipantshadhighereducation
qualications.
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Table A1. Demographic and professional prole of key informant participants
Participant characteristics n=16
Decade of birth
1960s 3
1970s 4
1980s 9
Time working in the area: 6–32 years (average 16)
Organisation type (not mutually exclusive)
Peer-based non-government organisation 10
University 4
Consultant/freelance 2
Health service 1
Priority population expertise (not mutually exclusive)
Trans and gender diverse people 9
People with HIV 8
Sex workers 7
Gay and bisexual men 7
People who inject drugs 6
Young people 2
Aboriginal and Torres Strait Islander People 1
Professional role (not mutually exclusive)
Advocacy 10
Education 7
Policy 7
Research 6
Health promotion 3
Professional jurisdiction
Mainly state-focused only 6
National-focused only 5
National and global focus 3
State and national focus 2
Global-focused only 1
Gender
Cis woman or female 7
Cis man or male 6
Non-binary 2
Trans man 1
Sexuality
Queer 5
Gay 4
Bisexual/pansexual 4
Straight 3
Cultural/language background
Anglo/Irish/European heritage 13
AsianorPacicIslanderheritage 2
Aboriginal or Torres Strait Islander heritage 1
Highest qualication
PhD 4
Masters 6
Graduate Diploma 2
Bachelor 1
Diploma 1
High school 2
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Appendix B: Community survey methods
A national online community survey was developed for this study, hosted on the UNSW Qualtrics
platform (Version 04/20) and conducted during April–June 2020. The survey took approximately
15 minutes to complete.
Thesurveyinstrumentwasdevelopedspecicallyforthisstudy,commencingwith22general
questions about demographics, digital technology access, and impacts of the COVID-19
pandemic, then 10 questions about health status, medication use, health care access, priority
populationidentication,andexperiencesofstigmaanddiscrimination.Another34questions
were grouped into four themed sections, which were displayed in randomised order for
participants:1)trust(ingeneral,organisations,andhealthcareservices);2)viewsontheuse
ofhealthdata;3)electronichealthrecords;4)useofdigitalhealthtechnologies.Someofthe
trustmeasureshadbeenvalidatedinotherstudies(Idler&Benyamini,1997;Lundmarketal.,
2016), and we drew from other survey measures of trust in digital health (Accenture Consulting,
2018;Bruce&Critchley,2017),butmostitemswerenewlydevelopedforthisproject.Thesurvey
instrument was piloted with our community partners and volunteers from personal networks
within the affected communities (see Acknowledgements).
Weaimedtosurveyaminimumof2,000adultslivinginAustralia(procientinEnglish)ontheir
viewsandpracticesrelatedtodigitalhealth,includingatleast100peoplewhoself-identied
witheachofthefourprioritypopulations.Thiswasconsideredtobesucientlylargetoconduct
meaningful comparisons between the general population subsample and the priority population
subgroups,whilealsoachievingasucientdiversityintermsofgender,ageandstate/territory
of residence.
The launch of the survey was delayed by the COVID-19 pandemic, as we believed it was
inappropriatetobeginpromotingasurveyonadifferenthealthtopicintherstfewweeks
of the Australian response to COVID-19. This provided us with additional time to develop a
fewquestionsspecictotheCOVID-19context,giventhishadbecomeanessentialissuefor
contextualisingandinterpretingthestudyndings.
The survey was soft launched on 18 April 2020 to assess early responses and functionality, with
active recruitment commencing after that. Participants were offered the opportunity to leave
their email address to take part in a prize draw for one of 20 gift cards worth $50 each. Emails
wererstsenttocommunitypartners,whotookontheprimaryresponsibilityforpromoting
the survey to priority population groups through their own communication channels, including
social media and newsletters. Recruitment of the priority populations could only happen with
the support of these partners, verifying that the research had been developed with and would
providebenetsforthesecommunities.Wealsobeganrecruitingthegeneralpopulationsample
through Facebook posts and paid advertising. When prospective participants clicked on an
advertisement, post or an email about the study, it took them to the study website, which took
them through the consent and eligibility process before participants could commence the
survey. Although we knew recruitment could prove challenging during the early weeks of the
COVID-19 pandemic, there was less success in reaching general population survey participants
through Facebook advertising than we had anticipated from our experience in previous studies.
To ensure we could meet our minimum targets, we engaged Qualtrics Research Services to
support the recruitment of the remaining general population survey participants. The minimum
targets were reached through this combined approach, and the survey was closed on 15 June
2020.
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Statistical analyses
AggregateddataforallndingsarepresentedintheAppendices.Participantswhowerenot
members of any of the eight priority population groups were categorised as ‘general population’
participants. Frequencies and proportions are reported for the total sample, all general
population participants, gay and bisexual men, people with HIV, sex workers, and trans and
gender diverse people.
ExploratoryanalyseswererstconductedatthebivariatelevelusingPearson’schi-squaredtest
tocomparegeneralpopulationparticipantswithparticipantswhoidentiedwithoneormore
priority population groups. However, owing to some participants being members of multiple
priority population groups and in order to obtain results which show variation based on priority
population characteristics, multivariate logistic regression analyses were used for all key
ndings.
These analyses determine independent associations between the four priority population
groupsthatwerespecicallyrecruitedforthisstudy(gayandbisexualmen,peoplewithHIV,sex
workers and trans and gender diverse people) and the outcomes of interest. The independent
variables for these analyses were the four priority population groups, relevant demographic
variables, and health and wellbeing status variables. These variables seek to control for
variations in sampling and the possible effect of having complex or multiple health conditions on
the outcomes of interest. The demographics included age, education (having a university degree
or not), and whether participants lived in a regional area. The health status variables included
whether participants had one or more long-term health conditions, whether they had poor/fair
health(asopposedtogood,verygoodorexcellent;self-rated),andwhetherparticipantswere
receiving mental health care. As discussed under the Health and Wellbeing section, priority
population groups were much more likely to report receiving mental health care (see Figure B3).
Interpretation of multivariate analyses
Wepresentthendingsfrommultivariateanalysesusing(adjusted)oddsratiosand95%
condenceintervalsingraphicalform.Thecondenceintervalsaredepictedwithahorizontal
line around the odds ratio plotted on the x-axis. No line or a short line indicates a narrow
condenceinterval.Ifthecondenceintervalfortheoddsratioincludesorcrosses1.00,the
resultforthatvariablewasnotstatisticallysignicantatp<0.5. Therefore, any horizontal line
that intersects the vertical red line at 1.00 indicates that the association between that variable
andtheoutcomewasnotstatisticallysignicant(whencontrollingfortheeffectoftheother
variables in the model). An odds ratio of less than 1.00 indicates a lower odds of association
between the independent variable and the outcome. An odds ratio of more than 1.00 indicates
greaterodds.Allmodelsincludedinthisreportweresignicantatp<0.5. Hosmer-Lemeshow
goodnessofttestswerenotviolated.
Due to the sensitive nature of health-related questions, survey participants were instructed
to leave an item blank if they were not comfortable responding to it. This accounts for small
reductions in the overall denominator for some measures, except where it is noted that the item
was only asked of certain participants, in which case the total number of cases or denominator
is provided. Missing data were treated using listwise deletion. All statistical analyses were
conducted using Stata Version 16.1.
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Appendix C: Participant characteristics and internet access
Table C1. Participant characteristics among Trust in Digital Health Survey participants, by priority population group
Totala
N=2,240
General
populationb
n=1,640
Gay and
bisexual
menc
n=277
People with
HIVc
n=107
Sex
workersc
n=139
Trans and
gender
diverse
peoplec
n=130
Age
18–29 611 (27.3) 421 (25.7) 62 (22.4) 7 (6.5) 61 (43.9) 53 (40.8)
30–44 668 (29.8) 473 (28.8) 87 (31.4) 28 (26.2) 47 (33.8) 45 (34.6)
45–59 590 (26.3) 423 (25.8) 96 (34.7) 54 (50.5) 30 (21.6) 24 (18.5)
60+ 371 (16.6) 323 (19.7) 32 (11.6) 18 (16.8) 1 (0.7) 8 (6.2)
State
Australian Capital Territory 66 (2.9) 43 (2.6) 8 (2.9) 5 (4.7) 5 (3.6) 3 (2.3)
New South Wales 781 (34.9) 531 (32.4) 134 (48.4) 63 (58.9) 41 (29.5) 47 (36.2)
Northern Territory 28 (1.2) 10 (0.6) 2 (0.7) 2 (1.9) 11 (7.9) 5 (3.8)
Queensland 391 (17.5) 305 (18.6) 36 (13.0) 10 (9.3) 25 (18.0) 17 (13.1)
South Australia 154 (6.9) 112 (6.8) 26 (9.4) 8 (7.5) 4 (2.9) 10 (7.7)
Tasmania 53 (2.4) 43 (2.6) 3 (1.1) – 4 (2.9) 2 (1.5)
Victoria 536 (23.9) 410 (25.0) 54 (19.5) 17 (15.9) 33 (23.7) 30 (23.1)
Western Australia 231 (10.3) 186 (11.3) 14 (5.1) 2 (1.9) 16 (11.5) 16 (12.3)
Region
Capital city 1,366 (61.0) 964 (58.8) 207 (74.7) 76 (71.0) 100 (71.9) 90 (69.2)
Other city 313 (14.0) 240 (14.6) 28 (10.1) 7 (6.5) 16 (11.5) 13 (10.0)
Regional centre/town 415 (18.5) 325 (19.8) 29 (10.5) 16 (15.0) 17 (12.2) 20 (15.4)
Rural or remote area 146 (6.5) 111 (6.8) 13 (4.7) 8 (7.5) 6 (4.3) 7 (5.4)
Education
High school 602 (26.9) 461 (28.1) 59 (21.3) 24 (22.4) 36 (25.9) 30 (23.1)
Tertiarydiploma/Tradecerticate/TAFE 584 (26.1) 444 (27.1) 54 (19.5) 33 (30.8) 34 (24.5) 23 (17.7)
University degree 1,054 (47.1) 735 (44.8) 164 (59.2) 50 (46.7) 69 (49.6) 77 (59.2)
Gender
Male 969 (43.3) 645 (39.3) 263 (94.9) 90 (84.1) 13 (9.4) 21 (16.2)
Female 1,182 (52.8) 982 (59.9) – 13 (12.1) 108 (77.7) 35 (26.9)
Non-binary 56 (2.5) – 9 (3.2) 4 (3.7) 13 (9.4) 56 (43.1)
Different identity or prefer not to say 33 (1.5) 13 (0.8) 5 (1.8) – 5 (3.6) 18 (13.8)
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 44
Totala
N=2,240
General
populationb
n=1,640
Gay and
bisexual
menc
n=277
People with
HIVc
n=107
Sex
workersc
n=139
Trans and
gender
diverse
peoplec
n=130
Sexuality
Heterosexual 1,622 (72.4) 1,489 (90.8) – 18 (16.8) 40 (28.8) 9 (6.9)
Lesbian 40 (1.8) 21 (1.3) – – 9 (6.5) 11 (8.5)
Gay 208 (9.3) – 205 (74.0) 68 (63.6) 5 (3.6) 6 (4.6)
Bisexual 162 (7.2) 68 (4.1) 39 (14.1) 5 (4.7) 31 (22.3) 23 (17.7)
Queer 55 (2.5) 6 (0.4) 7 (2.5) 4 (3.7) 27 (19.4) 27 (20.8)
Multiple identities or different term 118 (5.3) 31 (1.9) 26 (9.4) 10 (9.3) 25 (18.0) 52 (40.0)
No response 35 (1.6) 25 (1.5) – 2 (1.9) 2 (1.4) 2 (1.5)
Aboriginal and/or Torres Strait Islander heritage
No 2,174 (97.1) 1,640 (100.0) 269 (97.1) 103 (96.3) 132 (95.0) 123 (94.6)
Aboriginal 58 (2.6) – 8 (2.9) 3 (2.8) 6 (4.3) 6 (4.6)
Torres Strait Islander 5 (0.2) – – 1 (0.9) 1 (0.7) 1 (0.8)
Aboriginal and Torres Strait Islander 3 (0.1) – – – – –
Country of birth
Australia 1,685 (75.2) 1,216 (74.1) 220 (79.4) 81 (75.7) 107 (77.0) 98 (75.4)
Asia, New Zealand 211 (9.4) 167 (10.2) 15 (5.4) 9 (8.4) 11 (7.9) 8 (6.2)
Europe, United Kingdom, Ireland 210 (9.4) 156 (9.5) 29 (10.5) 11 (10.3) 9 (6.5) 15 (11.5)
Middle East 11 (0.5) 10 (0.6) 1 (0.4) 1 (0.9) – –
Africa 15 (0.7) 12 (0.7) 2 (0.7) – – 1 (0.8)
North America 30 (1.3) 18 (1.1) 2 (0.7) 1 (0.9) 6 (4.3) 5 (3.8)
South America, Caribbean 11 (0.5) 8 (0.5) 2 (0.7) – 1 (0.7) –
No response 67 (3.0) 53 (3.2) 6 (2.2) 4 (3.7) 5 (3.6) 3 (2.3)
Employment status (non-exclusive categories)
Full-time 754 (33.7) 543 (33.1) 124 (44.8) 40 (37.4) 27 (19.4) 36 (27.7)
Part-time 327 (14.6) 251 (15.3) 24 (8.7) 12 (11.2) 23 (16.5) 23 (17.7)
Casual 156 (7.0) 90 (5.5) 33 (11.9) 10 (9.3) 15 (10.8) 20 (15.4)
Self-employed 230 (10.3) 125 (7.6) 21 (7.6) 10 (9.3) 64 (46.0) 22 (16.9)
Receivinggovernmentbenets 329 (14.7) 200 (12.2) 52 (18.8) 38 (35.5) 33 (23.7) 36 (27.7)
Student 210 (9.4) 135 (8.2) 38 (13.7) 6 (5.6) 15 (10.8) 33 (25.4)
Unemployed 292 (13.0) 219 (13.4) 31 (11.2) 8 (7.5) 14 (10.1) 19 (14.6)
Unpaid carer 55 (2.5) 42 (2.6) 9 (3.2) 4 (3.7) 1 (0.7) 1 (0.8)
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Totala
N=2,240
General
populationb
n=1,640
Gay and
bisexual
menc
n=277
People with
HIVc
n=107
Sex
workersc
n=139
Trans and
gender
diverse
peoplec
n=130
Caring responsibilities (non-exclusive categories)
Parental (children 18 or under) 497 (22.2) 406 (24.8) 11 (4.0) 8 (7.5) 21 (15.1) 14 (10.8)
Other carer responsibilities 254 (11.3) 179 (10.9) 38 (13.7) 14 (13.1) 12 (8.6) 16 (12.3)
Income
Less than $40,000 859 (38.3) 622 (37.9) 98 (35.4) 48 (44.9) 57 (41.0) 59 (45.4)
$40,000–$79,999 650 (29.0) 495 (30.2) 67 (24.2) 19 (17.8) 40 (28.8) 31 (23.8)
$80,000–$120,000 376 (16.8) 267 (16.3) 64 (23.1) 24 (22.4) 17 (12.2) 20 (15.4)
More than $120,000 188 (8.4) 125 (7.6) 34 (12.3) 10 (9.3) 13 (9.4) 11 (8.5)
Prefer not to say 167 (7.5) 131 (8.0) 14 (5.1) 6 (5.6) 12 (8.6) 9 (6.9)
a The total column for the tables includes all participants, including the following priority population groups: Aboriginal and/or Torres Strait Islander people, gay and bisexual men, people with hepatitis B, people
with hepatitis C, people with HIV, people who inject drugs, sex workers and trans and gender diverse people.
b The general population comparison group includes all participants who were not a member of any of the eight priority population groups detailed in this report.
c The priority population groups are not mutually exclusive, i.e., some participants are included in multiple groups.
Table C2. Cross tabulations showing frequencies in which priority population members were members of one or more priority population
groups
Aboriginal
or/Torres
Strait
Islander
people
(n=67)
Gay or
bisexual
men
(n=277)
People with
hepatitis B
(n=15)
People with
hepatitis C
(n=27)
People with
HIV
(n=107)
People who
inject
drugs
(n=36)
Sex workers
(n=139)
Trans and
gender
diverse
people
(n=130)
Aboriginal or/Torres Strait Islander people 42 8 1 4 4 1 7 8
Gay or bisexual men 8 166 3 4 77 8 7 29
People with hepatitis B 1 3 9 2 1 1 1 0
People with hepatitis C 4 4 2 13 4 3 2 1
People with HIV 4 77 1 4 25 8 2 5
People who inject drugs 1 8 1 3 8 9 16 7
Sex workers 7 7 1 2 2 16 94 24
Trans and gender diverse people 8 29 0 1 5 7 24 70
Note: Frequencies in the shaded text boxes represent the number of participants in each priority population group who were not a member of any other group. The sum of frequencies per column exceeds the
column total due to some participants being members of more than two priority population groups.
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Table C3. Internet access among Trust in Digital Health Survey participants, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Quality of access to the internet (easeandspeedofaccess;self-rated)
Excellent;IcanaccessitwheneverIneed 1,364 (60.9) 987 (60.2) 198 (71.5) 75 (70.1) 74 (53.2) 83 (63.8)
Good;Icanaccessitmostofthetime 721 (32.2) 542 (33.0) 65 (23.5) 23 (21.5) 52 (37.4) 39 (30.0)
Average;Icanaccessitsomeofthetime 126 (5.6) 92 (5.6) 11 (4.0) 9 (8.4) 10 (7.2) 8 (6.2)
Poor;Imainlystruggletoaccessit 23 (1.0) 15 (0.9) 3 (1.1) – 3 (2.2) –
Non-existent;Ihavenoaccess 6 (0.3) 4 (0.2) – – – –
Devices used to access the internet (non-mutually-exclusive categories)
Desktop or laptop computer 1,971 (88.0) 1,462 (89.1) 248 (89.5) 89 (83.2) 115 (82.7) 119 (91.5)
Mobile or smartphone 1,954 (87.2) 1,400 (85.4) 261 (94.2) 99 (92.5) 134 (96.4) 125 (96.2)
Games console 472 (21.1) 324 (19.8) 61 (22.0) 16 (15.0) 23 (16.5) 38 (29.2)
Internet-connected television 815 (36.4) 594 (36.2) 133 (48.0) 38 (35.5) 35 (25.2) 43 (33.1)
Tablet 950 (42.4) 734 (44.8) 124 (44.8) 46 (43.0) 28 (20.1) 41 (31.5)
Wearable device 243 (10.8) 148 (9.0) 52 (18.8) 16 (15.0) 17 (12.2) 16 (12.3)
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Appendix D: Health and wellbeing
Table D1. Access to government subsidised health care among Trust in Digital Health Survey participants, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Access to Medicare 2,098 (93.7) 1,530 (93.3) 271 (97.8) 104 (97.2) 131 (94.2) 124 (95.4)
Medicare type N=2,098 n=1,530 n=271 n=104 n=131 n=124
Australian citizen or permanent resident 2,044 (97.4) 1,493 (97.6) 266 (98.2) 104 (100.0) 128 (97.7) 123 (99.2)
Visitor 12 (0.6) 5 (0.3) – – 1 (0.8) –
Temporary resident 13 (0.6) 9 (0.6) 2 (0.7) – 1 (0.8) –
Don’t know / no response 29 (1.4) 23 (1.5) 3 (1.1) – 1 (0.8) 1 (0.8)
Has a Health Care Card 823 (39.2) 585 (38.2) 95 (35.1) 55 (52.9) 54 (41.2) 52 (41.9)
Table D2. Health and wellbeing status among Trust in Digital Health Survey participants, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Self-assessed health status*
Poor 123 (5.5) 83 (5.1) 12 (4.3) 6 (5.6) 15 (10.8) 17 (13.1)
Fair 387 (17.3) 285 (17.4) 44 (15.9) 26 (24.3) 23 (16.5) 26 (20.0)
Good 729 (32.5) 537 (32.7) 92 (33.2) 29 (27.1) 39 (28.1) 46 (35.4)
Very good 711 (31.7) 525 (32.0) 97 (35.0) 29 (27.1) 45 (32.4) 26 (20.0)
Excellent 281 (12.5) 207 (12.6) 30 (10.8) 15 (14.0) 16 (11.5) 13 (10.0)
Prefer not to answer 9 (0.4) 3 (0.2) 2 (0.7) 2 (1.9) 1 (0.7) 2 (1.5)
Long-term (chronic) health conditions
One condition 544 (24.3) 383 (23.4) 81 (29.2) 39 (36.4) 38 (27.3) 23 (17.7)
Multiple conditions 530 (23.7) 313 (19.1) 96 (34.7) 61 (57.0) 48 (34.5) 66 (50.8)
No conditions 1,106 (49.4) 902 (55.0) 95 (34.3) 5 (4.7) 48 (34.5) 39 (30.0)
Prefer not to answer 60 (2.7) 42 (2.6) 5 (1.8) 2 (1.9) 5 (3.6) 2 (1.5)
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Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Reasons for attending health care services
Arthritis management 216 (9.6) 167 (10.2) 23 (8.3) 11 (10.3) 8 (5.8) 5 (3.8)
Back pain management 395 (17.6) 287 (17.5) 42 (15.2) 14 (13.1) 30 (21.6) 27 (20.8)
Cancer treatment or monitoring 69 (3.1) 49 (3.0) 9 (3.2) 8 (7.5) 3 (2.2) 2 (1.5)
Cardiovascular disease management 145 (6.5) 103 (6.3) 25 (9.0) 12 (11.2) 6 (4.3) 10 (7.7)
Chronic respiratory disease (incl. asthma) management 211 (9.4) 129 (7.9) 38 (13.7) 13 (12.1) 18 (12.9) 24 (18.5)
Diabetes management 141 (6.3) 99 (6.0) 19 (6.9) 6 (5.6) 6 (4.3) 4 (3.1)
Genderarmingcare 81 (3.6) – 23 (8.3) 1 (0.9) 15 (10.8) 81 (62.3)
Genderarmingrequirements 50 (2.2) – 16 (5.8) 2 (1.9) 9 (6.5) 46 (35.4)
Hepatitis B treatment or monitoring 7 (0.3) – 3 (1.1) 1 (0.9) – –
Hepatitis C treatment or management 13 (0.6) – 4 (1.4) 3 (2.8) 2 (1.4) –
HIV treatment or management 107 (4.8) – 77 (27.8) 107 (100.0) 2 (1.4) 5 (3.8)
Medical testing 737 (32.9) 498 (30.4) 93 (33.6) 24 (22.4) 87 (62.6) 73 (56.2)
Mental health care 638 (28.5) 357 (21.8) 115 (41.5) 44 (41.1) 86 (61.9) 90 (69.2)
Reproductive health care 128 (5.7) 85 (5.2) 4 (1.4) – 30 (21.6) 14 (10.8)
Sexual health testing, treatment or monitoring 339 (15.1) 76 (4.6) 127 (45.8) – 113 (81.3) 55 (42.3)
Sum of reasons endorsed for attending health services
None 47 (2.1) 43 (2.6) 2 (0.7) – 1 (0.7) –
One 1,278 (57.1) 1,085 (66.2) 93 (33.6) 23 (21.5) 23 (16.5) 18 (13.8)
2–3 644 (28.7) 410 (25.0) 115 (41.5) 50 (46.7) 54 (38.8) 43 (33.1)
4–5 224 (10.0) 91 (5.5) 56 (20.2) 25 (23.4) 44 (31.7) 54 (41.5)
Six or more 47 (2.1) 11 (0.7) 11 (4.0) 9 (8.4) 17 (12.2) 15 (11.5)
Number of doctor prescribed medications
None 856 (38.2) 719 (43.8) 56 (20.2) 1 (0.9) 43 (30.9) 13 (10.0)
1–3 1,001 (44.7) 688 (42.0) 153 (55.2) 60 (56.1) 60 (43.2) 76 (58.5)
4–9 315 (14.1) 187 (11.4) 57 (20.6) 39 (36.4) 33 (23.7) 37 (28.5)
More than 10 42 (1.9) 26 (1.6) 10 (3.6) 6 (5.6) 2 (1.4) 4 (3.1)
Prefer not to answer 26 (1.2) 20 (1.2) 1 (0.4) 1 (0.9) 1 (0.7) –
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 49
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Number of over-the-counter medications
None 1,132 (50.5) 871 (53.1) 130 (46.9) 42 (39.3) 53 (38.1) 40 (30.8)
1–3 962 (42.9) 683 (41.6) 131 (47.3) 56 (52.3) 66 (47.5) 74 (56.9)
4–9 110 (4.9) 60 (3.7) 16 (5.8) 9 (8.4) 15 (10.8) 15 (11.5)
More than 10 13 (0.6) 8 (0.5) – – 4 (2.9) 1 (0.8)
Prefer not to answer 23 (1.0) 18 (1.1) – – 1 (0.7) –
Health services attended in the past year
(non-mutually-exclusive categories)
General practice (GP) service 1,990 (88.8) 1,453 (88.6) 263 (94.9) 100 (93.5) 127 (91.4) 126 (96.9)
Pharmacy 1,551 (69.2) 1,084 (66.1) 227 (81.9) 94 (87.9) 117 (84.2) 116 (89.2)
Dentist 1,042 (46.5) 731 (44.6) 168 (60.6) 63 (58.9) 64 (46.0) 64 (49.2)
In-patient hospital 322 (14.4) 208 (12.7) 47 (17.0) 25 (23.4) 39 (28.1) 29 (22.3)
Out-patient hospital or specialist clinic 438 (19.6) 279 (17.0) 81 (29.2) 45 (42.1) 36 (25.9) 46 (35.4)
Community-based or peer-led sexual health clinic 105 (4.7) 18 (1.1) 38 (13.7) 15 (14.0) 34 (24.5) 20 (15.4)
Public sexual health clinic 214 (9.6) 29 (1.8) 84 (30.3) 48 (44.9) 79 (56.8) 26 (20.0)
Allied health service 519 (23.2) 339 (20.7) 84 (30.3) 30 (28.0) 45 (32.4) 57 (43.8)
Mental health service 573 (25.6) 293 (17.9) 118 (42.6) 52 (48.6) 84 (60.4) 91 (70.0)
Aboriginal community controlled health 16 (0.7) – 2 (0.7) 1 (0.9) 3 (2.2) 4 (3.1)
Alcohol or other drug service 50 (2.2) 12 (0.7) 8 (2.9) 6 (5.6) 17 (12.2) 13 (10.0)
Did not attend health services in the past year 78 (3.5) 68 (4.1) 3 (1.1) – – 1 (0.8)
Access to health care (self-rated)
Non-existent;Ihavenoaccess 9 (0.4) 5 (0.3) 1 (0.4) 1 (0.9) 1 (0.7) –
Poor;ImainlystruggletoaccesswhatIneed 59 (2.6) 36 (2.2) 5 (1.8) 4 (3.7) 8 (5.8) 9 (6.9)
Average;IcanaccesssomeofwhatIneed 283 (12.6) 187 (11.4) 29 (10.5) 16 (15.0) 28 (20.1) 35 (26.9)
Good;IcanaccessmostofwhatIneed 981 (43.8) 724 (44.1) 123 (44.4) 41 (38.3) 56 (40.3) 56 (43.1)
Excellent;IcanaccesswhateverIneed 904 (40.4) 685 (41.8) 119 (43.0) 45 (42.1) 46 (33.1) 30 (23.1)
Prefer not to answer 4 (0.2) 3 (0.2) – – – –
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 50
Table D3. Reported experiences of stigma among Trust in Digital Health Survey priority population groups in the last 12 months
Never Rarely Sometimes Often Always
Generala
Sexual orientation (n=580) 209 (36.0) 202 (34.8) 131 (22.6) 28 (4.8) 10 (1.7)
Gender experience or identity (n=128) 10 (7.8) 26 (20.3) 51 (39.8) 32 (25.0) 9 (7.0)
Sex work (n=139) 9 (6.5) 18 (12.9) 33 (23.7) 57 (41.0) 22 (15.8)
HIV status (n=106) 40 (37.7) 24 (22.6) 34 (32.1) 6 (5.7) 2 (1.9)
Healthcare settingsb
Sexual orientation (n=579) 357 (61.7) 128 (22.1) 68 (11.7) 19 (3.3) 7 (1.2)
Gender experience or identity (n=128) 34 (26.6) 30 (23.4) 41 (32.0) 16 (12.5) 7 (5.5)
Sex work (n=138) 25 (18.1) 24 (17.4) 49 (35.5) 29 (21.0) 11 (8.0)
HIV status (n=106) 50 (47.2) 35 (33.0) 16 (15.1) 4 (3.8) 1 (0.9)
Note: Data are n (%), where the denominator for the proportions is the row total.
a Examples of stigma or discrimination were given as ‘avoidance, pity, blame, shame, anxiety, rejection, verbal abuse, and bullying’.
b Participants were asked to what extent they agreed that health workers treated them negatively or different to other people due to the characteristics shown.
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 51
Figure D1. Factors associated with having one or more long-term medical conditions among
Trust in Digital Health Survey participants (N=2,240)
1.03
0.88
1.20
1.45
12.78
2.44
2.87
Age (years)
University degree
Lives in a regional area
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Priority Populations
0.00 10.00 20.00 30.00
Figure D2. Factors associated with poor/fair health (self-rated) among Trust in Digital Health
Survey participants (N=2,240)
1.02
0.58
1.16
0.77
1.64
1.46
2.16
Age (years)
University degree
Lives in a regional area
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Priority Populations
0.50 1.00 1.50 2.00 2.50 3.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 52
Figure D3. Factors associated with receiving mental health care among Trust in Digital Health
Survey participants (N=2,240)
0.98
0.79
1.04
1.89
1.69
3.80
5.30
Age (years)
University degree
Lives in a regional area
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Priority Populations
0.00 2.00 4.00 6.00 8.00
Figure D4. Factors associated with taking four or more prescription medications among Trust
in Digital Health Survey participants (N=2,240)
1.05
0.81
1.04
8.04
3.53
1.96
1.25
2.04
2.25
2.54
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 5.00 10.00 15.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 53
Figure D5. Factors associated with good/excellent access to health care (e.g. affordability/
location) among Trust in Digital Health Survey participants (N=2,240)
1.00
1.29
0.53
1.51
0.50
0.53
0.31
Age (years)
University degree
Lives in a regional area
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Priority Populations
0.00 0.50 1.00 1.50 2.00 2.50
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 54
Appendix E: Trust
Table E1. Generalised trust and trust in institutions among Trust in Digital Health Survey participants, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Generalised trust scale
Less trusting 849 (37.9) 587 (35.8) 106 (38.3) 55 (51.4) 84 (60.4) 56 (43.1)
Neutral 635 (28.4) 475 (29.0) 69 (24.9) 20 (18.7) 35 (25.2) 43 (33.1)
More trusting 755 (33.7) 577 (35.2) 102 (36.8) 32 (29.9) 20 (14.4) 31 (23.8)
Digital technologya
Distrust a great deal/Distrust 851 (38.0) 566 (34.6) 119 (43.0) 54 (50.5) 84 (60.4) 72 (55.4)
Neither 780 (34.9) 607 (37.1) 78 (28.2) 32 (29.9) 36 (25.9) 34 (26.2)
Trust/Trust a great deal 607 (27.1) 465 (28.4) 80 (28.9) 21 (19.6) 19 (13.7) 24 (18.5)
Charities
Distrust a great deal/Distrust 628 (28.1) 435 (26.6) 76 (27.4) 32 (29.9) 67 (48.2) 42 (32.3)
Neither 837 (37.4) 603 (36.9) 109 (39.4) 44 (41.1) 51 (36.7) 62 (47.7)
Trust/Trust a great deal 771 (34.5) 598 (36.6) 92 (33.2) 31 (29.0) 21 (15.1) 26 (20.0)
Commercial media
Distrust a great deal/Distrust 1,326 (59.3) 889 (54.3) 208 (75.1) 83 (77.6) 114 (82.0) 118 (90.8)
Neither 635 (28.4) 521 (31.8) 49 (17.7) 19 (17.8) 21 (15.1) 10 (7.7)
Trust/Trust a great deal 276 (12.3) 227 (13.9) 20 (7.2) 5 (4.7) 4 (2.9) 2 (1.5)
Non-commercial media
Distrust a great deal/Distrust 619 (27.7) 468 (28.6) 72 (26.0) 32 (29.9) 35 (25.2) 36 (27.7)
Neither 1,006 (45.0) 760 (46.5) 105 (37.9) 42 (39.3) 61 (43.9) 59 (45.4)
Trust/Trust a great deal 610 (27.3) 407 (24.9) 100 (36.1) 33 (30.8) 43 (30.9) 35 (26.9)
State government
Distrust a great deal/Distrust 674 (30.2) 411 (25.1) 111 (40.1) 57 (53.3) 81 (58.3) 67 (51.5)
Neither 766 (34.3) 565 (34.6) 91 (32.9) 28 (26.2) 43 (30.9) 49 (37.7)
Trust/Trust a great deal 795 (35.6) 659 (40.3) 75 (27.1) 22 (20.6) 15 (10.8) 14 (10.8)
Federal government
Distrust a great deal/Distrust 847 (37.9) 501 (30.6) 161 (58.1) 63 (59.4) 97 (69.8) 100 (77.5)
Neither 647 (29.0) 507 (31.0) 63 (22.7) 27 (25.5) 32 (23.0) 21 (16.3)
Trust/Trust a great deal 740 (33.1) 627 (38.3) 53 (19.1) 16 (15.1) 10 (7.2) 8 (6.2)
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 55
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
The public service
Distrust a great deal/Distrust 527 (23.6) 356 (21.8) 68 (24.5) 39 (36.4) 52 (37.7) 46 (35.4)
Neither 902 (40.4) 668 (40.9) 105 (37.9) 38 (35.5) 57 (41.3) 54 (41.5)
Trust/Trust a great deal 804 (36.0) 610 (37.3) 104 (37.5) 30 (28.0) 29 (21.0) 30 (23.1)
Major international companies
Distrust a great deal/Distrust 1,114 (49.8) 712 (43.5) 188 (67.9) 79 (73.8) 107 (77.0) 112 (86.2)
Neither 793 (35.5) 654 (40.0) 70 (25.3) 18 (16.8) 27 (19.4) 14 (10.8)
Trust/Trust a great deal 328 (14.7) 269 (16.5) 19 (6.9) 10 (9.3) 5 (3.6) 4 (3.1)
Major Australian companies
Distrust a great deal/Distrust 918 (41.1) 554 (33.9) 163 (58.8) 69 (64.5) 107 (77.0) 109 (83.8)
Neither 808 (36.1) 655 (40.0) 80 (28.9) 28 (26.2) 25 (18.0) 16 (12.3)
Trust/Trust a great deal 510 (22.8) 427 (26.1) 34 (12.3) 10 (9.3) 7 (5.0) 5 (3.8)
The environmental movement
Distrust a great deal/Distrust 537 (24.0) 425 (26.0) 53 (19.1) 22 (20.8) 28 (20.1) 16 (12.4)
Neither 917 (41.0) 670 (41.0) 120 (43.3) 49 (46.2) 52 (37.4) 62 (48.1)
Trust/Trust a great deal 780 (34.9) 540 (33.0) 104 (37.5) 35 (33.0) 59 (42.4) 51 (39.5)
Religious organisations
Distrust a great deal/Distrust 1,268 (56.8) 815 (49.9) 230 (83.0) 85 (79.4) 112 (80.6) 116 (89.2)
Neither 579 (25.9) 490 (30.0) 34 (12.3) 13 (12.1) 19 (13.7) 10 (7.7)
Trust/Trust a great deal 387 (17.3) 329 (20.1) 13 (4.7) 9 (8.4) 8 (5.8) 4 (3.1)
Scientists
Distrust a great deal/Distrust 211 (9.4) 147 (9.0) 22 (7.9) 14 (13.1) 21 (15.1) 13 (10.0)
Neither 563 (25.2) 437 (26.7) 42 (15.2) 26 (24.3) 31 (22.3) 26 (20.0)
Trust/Trust a great deal 1,461 (65.4) 1,051 (64.3) 213 (76.9) 67 (62.6) 87 (62.6) 91 (70.0)
Universities
Distrust a great deal/Distrust 347 (15.6) 241 (14.7) 42 (15.3) 22 (20.8) 37 (26.6) 31 (23.8)
Neither 832 (37.3) 628 (38.4) 78 (28.4) 34 (32.1) 55 (39.6) 45 (34.6)
Trust/Trust a great deal 1,052 (47.2) 765 (46.8) 155 (56.4) 50 (47.2) 47 (33.8) 54 (41.5)
Note: A small number of participants chose not to respond to some survey items. Frequencies may therefore be lower than the denominator for each column due to missing data. The items relating to trust in
peopleandorganisationshavebeenmodiedfromtheSwinburneNationalTechnologyandSocietyMonitor2017(Bruce&Critchley,2017).Specicexamplesforeachitem,e.g.,namesofcompanies,werenot
provided to avoid priming responses.
a Participants were asked: Generally speaking, how much do you trust digital technologies and systems with your personal information, such as full name, date of birth, address and credit card?
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 56
Table E2. Trust in health care services to keep medical information private and condential among Trust in Digital Health Survey
participants, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
General practice (GP) service
Distrust a great deal/Distrust 137 (6.9) 81 (5.6) 14 (5.3) 5 (5.0) 27 (21.6) 13 (10.3)
Neither 273 (13.7) 194 (13.4) 32 (12.2) 17 (17.0) 24 (19.2) 19 (15.1)
Trust/Trust a great deal 1,576 (79.4) 1,176 (81.0) 217 (82.5) 78 (78.0) 74 (59.2) 94 (74.6)
Pharmacy
Distrust a great deal/Distrust 139 (9.0) 82 (7.6) 15 (6.6) 6 (6.4) 25 (21.4) 20 (17.2)
Neither 336 (21.7) 189 (17.5) 63 (27.8) 28 (29.8) 44 (37.6) 38 (32.8)
Trust/Trust a great deal 1,071 (69.3) 808 (74.9) 149 (65.6) 60 (63.8) 48 (41.0) 58 (50.0)
Dentist
Distrust a great deal/Distrust 68 (6.5) 33 (4.5) 15 (8.9) 6 (9.5) 13 (20.3) 6 (9.4)
Neither 207 (19.9) 125 (17.1) 32 (19.0) 14 (22.2) 26 (40.6) 19 (29.7)
Trust/Trust a great deal 767 (73.6) 573 (78.4) 121 (72.0) 43 (68.3) 25 (39.1) 39 (60.9)
In-patient hospital
Distrust a great deal/Distrust 34 (10.6) 13 (6.3) 3 (6.4) 2 (8.0) 11 (28.2) 10 (34.5)
Neither 42 (13.1) 21 (10.1) 5 (10.6) 3 (12.0) 13 (33.3) 6 (20.7)
Trust/Trust a great deal 245 (76.3) 173 (83.6) 39 (83.0) 20 (80.0) 15 (38.5) 13 (44.8)
Out-patient hospital or specialist clinic
Distrust a great deal/Distrust 42 (9.6) 20 (7.2) 5 (6.2) 2 (4.4) 9 (25.0) 9 (19.6)
Neither 63 (14.4) 29 (10.4) 12 (14.8) 8 (17.8) 10 (27.8) 13 (28.3)
Trust/Trust a great deal 332 (76.0) 229 (82.4) 64 (79.0) 35 (77.8) 17 (47.2) 24 (52.2)
Community-based or peer-led sexual health clinic
Distrust a great deal/Distrust 6 (5.8) 1 (5.9) 2 (5.3) 2 (13.3) 3 (8.8) 2 (10.0)
Neither 19 (18.3) 7 (41.2) 3 (7.9) 2 (13.3) 5 (14.7) 2 (10.0)
Trust/Trust a great deal 79 (76.0) 9 (52.9) 33 (86.8) 11 (73.3) 26 (76.5) 16 (80.0)
Public sexual health clinic
Distrust a great deal/Distrust 27 (12.6) 5 (17.2) 7 (8.3) 6 (12.5) 11 (13.9) 6 (23.1)
Neither 36 (16.8) 4 (13.8) 11 (13.1) 7 (14.6) 18 (22.8) 4 (15.4)
Trust/Trust a great deal 151 (70.6) 20 (69.0) 66 (78.6) 35 (72.9) 50 (63.3) 16 (61.5)
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 57
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Allied health service
Distrust a great deal/Distrust 36 (6.9) 16 (4.7) 4 (4.8) 3 (10.0) 6 (13.3) 4 (7.0)
Neither 84 (16.2) 49 (14.5) 13 (15.5) 6 (20.0) 14 (31.1) 11 (19.3)
Trust/Trust a great deal 398 (76.8) 273 (80.8) 67 (79.8) 21 (70.0) 25 (55.6) 42 (73.7)
Mental health service
Distrust a great deal/Distrust 50 (8.8) 19 (6.5) 7 (5.9) 4 (7.7) 15 (17.9) 6 (6.6)
Neither 55 (9.6) 27 (9.3) 11 (9.3) 4 (7.7) 9 (10.7) 10 (11.0)
Trust/Trust a great deal 466 (81.6) 245 (84.2) 100 (84.7) 44 (84.6) 60 (71.4) 75 (82.4)
Aboriginal community controlled health
Distrust a great deal/Distrust 1 (6) 1 (17) – – – –
Neither 2 (12) 1 (17) – – 1 (33) –
Trust/Trust a great deal 13 (81) 4 (67) 2 (100) 1 (100) 2 (67) 4 (100)
Alcohol or other drug service
Distrust a great deal/Distrust 5 (10) 2 (17) – – 2 (12) 1 (8)
Neither 18 (36) 3 (25) 3 (38) 3 (50) 7 (41) 4 (31)
Trust/Trust a great deal 27 (54) 7 (58) 5 (62) 3 (50) 8 (47) 8 (62)
Note: Participants were only asked about the health care services that they had attended in the past year. The data represented by n (%) therefore sum to the total number of par ticipants who had used these
services, excluding small numbers of participants who chose not to respond to the item.
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 58
Figure E1. Factors associated with generalised trust (‘Most people can be trusted’) among
Trust in Digital Health Survey participants (n=2,239)
1.02
1.30
0.81
1.09
0.61
1.17
1.15
0.61
0.34
0.65
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
Figure E2. Factors associated with trust in general practice (GP) services to keep information
private and condential among Trust in Digital Health Survey participants who had attended a
GP in the past year (n=1,986)
1.01
1.02
0.96
1.07
0.79
1.09
1.31
0.65
0.38
0.89
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 59
Figure E3. Factors associated with trust in pharmacies to keep information private and
condential among Trust in Digital Health Survey participants who had attended a pharmacy in
the past year (n=1,546)
1.01
0.71
1.15
0.83
1.02
0.97
0.96
0.73
0.33
0.53
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
Figure E4. Factors associated with trust in dentists to keep information private and
condential among Trust in Digital Health Survey participants who had attended a dentist in
the past year (n=1,042)
1.01
0.98
0.90
0.77
0.84
0.96
0.92
0.74
0.24
0.66
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
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Figure E5. Factors associated with trust in in-patient hospitals to keep information private and
condential among Trust in Digital Health Survey participants who had attended an in-patient
hospital in the past year (n=321)
1.03
0.72
1.18
1.17
0.70
0.88
2.72
0.41
0.20
0.23
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 2.00 4.00 6.00 8.00
Figure E6. Factors associated with trust in out-patient hospitals and specialist clinics to keep
information private and condential among Trust in Digital Health Survey participants who had
attended an out-patient hospital or a specialist clinic in the past year (n=437)
1.02
0.75
1.18
1.20
0.73
0.65
1.40
0.73
0.38
0.46
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 1.00 2.00 3.00
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Appendix F: My Health Record
Table F1. Access to and knowledge about My Health Record among Trust in Digital Health Survey participants, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Had participants ever accessed My Health Record?
Yes 523 (23.3) 390 (23.8) 83 (30.0) 18 (16.8) 21 (15.1) 26 (20.0)
Accessed record but since deleted it 116 (5.2) 67 (4.1) 18 (6.5) 8 (7.5) 10 (7.2) 16 (12.3)
Opted out or deleted record 542 (24.2) 300 (18.3) 103 (37.2) 51 (47.7) 68 (48.9) 63 (48.5)
No, but they do have one 577 (25.8) 486 (29.6) 46 (16.6) 20 (18.7) 12 (8.6) 15 (11.5)
No, they’re not eligible for a record 78 (3.5) 61 (3.7) 7 (2.5) 2 (1.9) 5 (3.6) 2 (1.5)
Don’t know 404 (18.0) 336 (20.5) 20 (7.2) 8 (7.5) 23 (16.5) 8 (6.2)
Knowledge about My Health Record (self-reported)
Nothing 300 (13.4) 254 (15.5) 12 (4.3) 6 (5.6) 16 (11.5) 4 (3.1)
A small amount 772 (34.5) 606 (37.0) 83 (30.0) 32 (29.9) 40 (28.8) 32 (24.6)
A fair amount 523 (23.3) 358 (21.8) 80 (28.9) 28 (26.2) 34 (24.5) 34 (26.2)
Quite a bit 434 (19.4) 295 (18.0) 65 (23.5) 24 (22.4) 35 (25.2) 28 (21.5)
A lot 211 (9.4) 127 (7.7) 37 (13.4) 17 (15.9) 14 (10.1) 32 (24.6)
How participants learned of My Health Record (if they knew about it) (N=1,940) (n=1,386) (n=265) (n=101) (n=123) (n=126)
Conversation with doctor or another health professional 560 (28.9) 392 (28.3) 89 (33.6) 50 (49.5) 26 (21.1) 36 (28.6)
Conversations with friends or family 585 (30.2) 369 (26.6) 93 (35.1) 34 (33.7) 55 (44.7) 54 (42.9)
Information provided in media articles, including social media 950 (49.0) 611 (44.1) 176 (66.4) 64 (63.4) 73 (59.3) 87 (69.0)
Information provided by a community organisation 305 (15.7) 125 (9.0) 68 (25.7) 38 (37.6) 66 (53.7) 57 (45.2)
Information provided in government advertising campaigns 858 (44.2) 614 (44.3) 142 (53.6) 50 (49.5) 40 (32.5) 59 (46.8)
Another way 195 (10.1) 112 (8.1) 43 (16.2) 10 (9.9) 18 (14.6) 23 (18.3)
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Table F2. Use of My Health Record among Trust in Digital Health Survey participants who had a record, by priority population group
Total
N=1,100
General
population
n=876
Gay and
bisexual
men
n=129
People with
HIV
n=38
Sex
workers
n=33
Trans and
gender
diverse
people
n=41
Frequency of access to My Health Record
Never accessed it 577 (52.5) 486 (55.5) 46 (35.7) 20 (52.6) 12 (36.4) 15 (36.6)
Accessed it more than one year ago 85 (7.7) 62 (7.1) 17 (13.2) 3 (7.9) 4 (12.1) 3 (7.3)
Accessed it once or twice (past year) 378 (34.4) 283 (32.3) 62 (48.1) 13 (34.2) 13 (39.4) 19 (46.3)
Accessed it monthly (past year) 57 (5.2) 44 (5.0) 4 (3.1) 2 (5.3) 4 (12.1) 4 (9.8)
Accessed it weekly (past year) 3 (0.3) 1 (0.1) – – – –
Primary reason for using My Health Record
(participants who had accessed their record) (N=523) (n=390) (n=83) (n=18) (n=21) (n=26)
Keep me informed 109 (20.8) 84 (21.5) 14 (16.9) 2 (11.1) 1 (4.8) 5 (19.2)
Know what is in my record 244 (46.7) 178 (45.6) 46 (55.4) 7 (38.9) 7 (33.3) 12 (46.2)
Make sure record is accurate 99 (18.9) 74 (19.0) 15 (18.1) 5 (27.8) 6 (28.6) 5 (19.2)
Help make medical decisions 21 (4.0) 19 (4.9) – 1 (5.6) – 1 (3.8)
Track progression of illness or disease 16 (3.1) 11 (2.8) 1 (1.2) – 4 (19.0) 2 (7.7)
Other 29 (5.5) 19 (4.9) 7 (8.4) 3 (16.7) 3 (14.3) 1 (3.8)
Interactions on My Health Record
(participants who had accessed their record) (N=523) (n=390) (n=83) (n=18) (n=21) (n=26)
Added personal notes 149 (28.5) 110 (28.2) 20 (24.1) 5 (27.8) 10 (47.6) 12 (46.2)
Chose what content can be seen 127 (24.3) 89 (22.8) 21 (25.3) 8 (44.4) 8 (38.1) 12 (46.2)
Invited people to access the record 39 (7.5) 30 (7.7) 1 (1.2) 1 (5.6) 4 (19.0) 3 (11.5)
Set health care service access restrictions 90 (17.2) 63 (16.2) 11 (13.3) 5 (27.8) 5 (23.8) 10 (38.5)
Set a code on the record or to certain documents 44 (8.4) 29 (7.4) 8 (9.6) 6 (33.3) 5 (23.8) 5 (19.2)
Sethealthcareaccessnotications 94 (18.0) 59 (15.1) 20 (24.1) 8 (44.4) 7 (33.3) 13 (50.0)
Information that is useful (participants with a record)
Test results 728 (66.2) 574 (65.5) 91 (70.5) 25 (65.8) 23 (69.7) 33 (80.5)
Physician notes 606 (55.1) 488 (55.7) 71 (55.0) 20 (52.6) 19 (57.6) 24 (58.5)
Medication history 646 (58.7) 508 (58.0) 87 (67.4) 22 (57.9) 19 (57.6) 27 (65.9)
Immunisation history 629 (57.2) 501 (57.2) 82 (63.6) 20 (52.6) 18 (54.5) 29 (70.7)
Personal information 402 (36.5) 314 (35.8) 49 (38.0) 13 (34.2) 12 (36.4) 21 (51.2)
Billing history 233 (21.2) 180 (20.5) 31 (24.0) 6 (15.8) 10 (30.3) 11 (26.8)
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Total
N=1,100
General
population
n=876
Gay and
bisexual
men
n=129
People with
HIV
n=38
Sex
workers
n=33
Trans and
gender
diverse
people
n=41
Benets of My Health Record
Improved my care 529 (48.1) 434 (49.5) 57 (44.2) 15 (39.5) 15 (45.5) 19 (46.3)
Healthcare access medical history 695 (63.2) 555 (63.4) 89 (69.0) 25 (65.8) 16 (48.5) 27 (65.9)
Convenience 665 (60.5) 536 (61.2) 81 (62.8) 27 (71.1) 15 (45.5) 27 (65.9)
Saved time 304 (27.6) 251 (28.7) 27 (20.9) 8 (21.1) 8 (24.2) 6 (14.6)
Make medical decisions 186 (16.9) 157 (17.9) 15 (11.6) 4 (10.5) 7 (21.2) 3 (7.3)
Keep track of medications 234 (21.3) 194 (22.1) 21 (16.3) 12 (31.6) 8 (24.2) 5 (12.2)
Make decisions for someone I care for 114 (10.4) 106 (12.1) 3 (2.3) 1 (2.6) 1 (3.0) 1 (2.4)
Improved care of family or friend 94 (8.5) 83 (9.5) 5 (3.9) 2 (5.3) 2 (6.1) –
Nobenets 142 (12.9) 109 (12.4) 17 (13.2) 7 (18.4) 7 (21.2) 5 (12.2)
Doctors’ use of My Health Record
Doctors accessed information 218 (19.8) 182 (20.8) 15 (11.6) 8 (21.1) 6 (18.2) 5 (12.2)
Doctors added information 272 (24.7) 225 (25.7) 21 (16.3) 12 (31.6) 6 (18.2) 9 (22.0)
Doctors haven’t used it 140 (12.7) 101 (11.5) 24 (18.6) 7 (18.4) 6 (18.2) 11 (26.8)
Don’t know if doctors used it 594 (54.0) 473 (54.0) 79 (61.2) 17 (44.7) 18 (54.5) 19 (46.3)
Concerns about My Health Record
I do not trust that all health professionals will treat me with respect and
care 155 (14.1) 97 (11.1) 25 (19.4) 6 (15.8) 16 (48.5) 17 (41.5)
Iamnotcondentthegovernmentwillprotectmydataprivacy
adequately 350 (31.8) 250 (28.5) 65 (50.4) 18 (47.4) 16 (48.5) 21 (51.2)
My data might be used for commercial purposes 313 (28.5) 226 (25.8) 56 (43.4) 12 (31.6) 18 (54.5) 16 (39.0)
My data might be used for research without my consent 279 (25.4) 204 (23.3) 43 (33.3) 9 (23.7) 15 (45.5) 16 (39.0)
My data might be shared between government agencies without my
consent 338 (30.7) 229 (26.1) 66 (51.2) 14 (36.8) 19 (57.6) 26 (63.4)
My medical information might be hacked or leaked 365 (33.2) 266 (30.4) 60 (46.5) 11 (28.9) 16 (48.5) 24 (58.5)
My data might be used by the government in ways that disadvantage me 288 (26.2) 195 (22.3) 56 (43.4) 15 (39.5) 20 (60.6) 18 (43.9)
My doctor told me I should opt out 18 (1.6) 12 (1.4) 3 (2.3) 3 (7.9) 1 (3.0) –
Another person or organisation told me to opt out 15 (1.4) 8 (0.9) 3 (2.3) 1 (2.6) 4 (12.1) 3 (7.3)
Another concern 31 (2.8) 22 (2.5) 6 (4.7) – 1 (3.0) 3 (7.3)
No concern 364 (33.1) 322 (36.8) 25 (19.4) 7 (18.4) 6 (18.2) 3 (7.3)
Note: Two items were asked of participants who had accessed their electronic health record. The denominator is the column total except for these two items where different denominators are provided.
Frequencies and proportions from small group sizes should be interpreted with caution.
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Table F3. Reasons for opting out of My Health Record and information that might have been useful among Trust in Digital Health Survey
participants who had opted out of My Health Record or deleted their record, by priority population group
Total
N=658
General
population
n=367
Gay and
bisexual
men
n=121
People with
HIV
n=59
Sex
workers
n=78
Trans and
gender
diverse
people
n=79
Reason for opting out or deleting record
(non-mutually-exclusive categories)
I do not trust that all health professionals will treat me with respect and
care 301 (45.7) 125 (34.1) 63 (52.1) 36 (61.0) 71 (91.0) 58 (73.4)
Iamnotcondentthegovernmentwillprotectmydataprivacy
adequately 522 (79.3) 278 (75.7) 110 (90.9) 51 (86.4) 71 (91.0) 74 (93.7)
My data might be used for commercial purposes 378 (57.4) 194 (52.9) 85 (70.2) 46 (78.0) 48 (61.5) 50 (63.3)
My data might be used for research without my consent 291 (44.2) 156 (42.5) 55 (45.5) 34 (57.6) 47 (60.3) 43 (54.4)
My data might be shared between government agencies without my
consent 443 (67.3) 221 (60.2) 101 (83.5) 48 (81.4) 72 (92.3) 65 (82.3)
My medical information might be hacked or leaked 442 (67.2) 231 (62.9) 94 (77.7) 48 (81.4) 59 (75.6) 61 (77.2)
My data might be used by the government in ways that disadvantage me 412 (62.6) 189 (51.5) 97 (80.2) 48 (81.4) 69 (88.5) 68 (86.1)
My doctor told me I should opt out 41 (6.2) 15 (4.1) 15 (12.4) 12 (20.3) 3 (3.8) 7 (8.9)
Another person or organisation told me to opt out 34 (5.2) 12 (3.3) 8 (6.6) 4 (6.8) 7 (9.0) 10 (12.7)
Another reason 63 (9.6) 28 (7.6) 16 (13.2) 6 (10.2) 8 (10.3) 11 (13.9)
No reason 14 (2.1) 9 (2.5) 1 (0.8) – – –
Information that might have been useful in a record
(non-mutually-exclusive categories)
Test results 362 (55.0) 198 (54.0) 83 (68.6) 37 (62.7) 39 (50.0) 44 (55.7)
Physician notes 309 (47.0) 169 (46.0) 72 (59.5) 33 (55.9) 29 (37.2) 37 (46.8)
Medication history 334 (50.8) 186 (50.7) 73 (60.3) 38 (64.4) 32 (41.0) 36 (45.6)
Immunisation history 363 (55.2) 192 (52.3) 82 (67.8) 36 (61.0) 41 (52.6) 48 (60.8)
Personal information 136 (20.7) 76 (20.7) 33 (27.3) 14 (23.7) 11 (14.1) 18 (22.8)
Billing history 112 (17.0) 63 (17.2) 23 (19.0) 9 (15.3) 11 (14.1) 21 (26.6)
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Table F4. Willingness to share My Health Record data with health services among Trust in Digital Health Survey participantsa, by priority
population group
Total General
population
Gay and
bisexual
men
People with
HIV
Sex
workers
Trans and
gender
diverse
people
General practice (GP) service
Not at all willing/Not willing 240 (11.1) 123 (7.8) 32 (11.9) 24 (22.9) 41 (31.1) 26 (20.5)
Neither 248 (11.5) 188 (11.9) 14 (5.2) 5 (4.8) 17 (12.9) 14 (11.0)
Willing/Very willing 1,667 (77.4) 1,264 (80.3) 224 (83.0) 76 (72.4) 74 (56.1) 87 (68.5)
Pharmacy
Not at all willing/Not willing 541 (25.2) 311 (19.8) 85 (31.6) 37 (35.2) 70 (53.4) 69 (54.3)
Neither 404 (18.8) 317 (20.2) 39 (14.5) 17 (16.2) 16 (12.2) 16 (12.6)
Willing/Very willing 1,204 (56.0) 943 (60.0) 145 (53.9) 51 (48.6) 45 (34.4) 42 (33.1)
Dentist
Not at all willing/Not willing 520 (24.2) 296 (18.8) 79 (29.4) 43 (41.0) 69 (52.7) 65 (51.2)
Neither 408 (19.0) 308 (19.6) 49 (18.2) 11 (10.5) 17 (13.0) 24 (18.9)
Willing/Very willing 1,224 (56.9) 970 (61.6) 141 (52.4) 51 (48.6) 45 (34.4) 38 (29.9)
In-patient hospital
Not at all willing/Not willing 286 (13.3) 159 (10.1) 28 (10.4) 21 (20.0) 54 (41.2) 41 (32.3)
Neither 276 (12.8) 208 (13.2) 25 (9.3) 10 (9.5) 17 (13.0) 15 (11.8)
Willing/Very willing 1,588 (73.9) 1,205 (76.7) 216 (80.3) 74 (70.5) 60 (45.8) 71 (55.9)
Out-patient hospital or specialist clinic
Not at all willing/Not willing 339 (15.8) 196 (12.5) 36 (13.4) 21 (20.0) 59 (45.0) 45 (35.4)
Neither 339 (15.8) 255 (16.2) 33 (12.3) 13 (12.4) 18 (13.7) 16 (12.6)
Willing/Very willing 1,471 (68.5) 1,120 (71.3) 200 (74.3) 71 (67.6) 54 (41.2) 66 (52.0)
Community-based or peer-led sexual health clinicb
Not at all willing/Not willing 18 (18.0) 2 (12.5) 6 (16.2) 4 (26.7) 8 (25.0) 8 (42.1)
Neither 11 (11.0) 2 (12.5) 2 (5.4) 2 (13.3) 6 (18.8) –
Willing/Very willing 71 (71.0) 12 (75.0) 29 (78.4) 9 (60.0) 18 (56.2) 11 (57.9)
Public sexual health clinicb
Not at all willing/Not willing 47 (23.3) 6 (23.1) 10 (12.2) 9 (18.8) 26 (35.6) 8 (32.0)
Neither 15 (7.4) 3 (11.5) 4 (4.9) 4 (8.3) 8 (11.0) 2 (8.0)
Willing/Very willing 140 (69.3) 17 (65.4) 68 (82.9) 35 (72.9) 39 (53.4) 15 (60.0)
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Total General
population
Gay and
bisexual
men
People with
HIV
Sex
workers
Trans and
gender
diverse
people
Allied health serviceb
Not at all willing/Not willing 122 (23.8) 57 (17.1) 19 (22.6) 9 (30.0) 20 (44.4) 25 (43.9)
Neither 67 (13.1) 51 (15.3) 11 (13.1) 5 (16.7) 2 (4.4) 7 (12.3)
Willing/Very willing 323 (63.1) 225 (67.6) 54 (64.3) 16 (53.3) 23 (51.1) 25 (43.9)
Mental health serviceb
Not at all willing/Not willing 107 (19.2) 36 (12.7) 20 (17.2) 11 (22.0) 29 (35.8) 27 (30.0)
Neither 50 (9.0) 34 (12.0) 6 (5.2) 2 (4.0) 5 (6.2) 6 (6.7)
Willing/Very willing 399 (71.8) 213 (75.3) 90 (77.6) 37 (74.0) 47 (58.0) 57 (63.3)
Aboriginal community controlled healthc
Not at all willing/Not willing 4 (25.0)
Neither 4 (25.0)
Willing/Very willing 8 (50.0)
Alcohol or other drug servicec
Not at all willing/Not willing 14 (30.0)
Neither 7 (15.0)
Willing/Very willing 26 (55.0)
a The denominators (N/n) vary for each health care ser vice because a small number of participants chose not to respond to some survey items. These items also excluded par ticipants who reported they were
not eligible for a My Health Record. The total number of responses per item can therefore be calculated from the sum of frequencies per service per column.
b Participants who had attended these services in the past year. The data represented by n (%) therefore sum to the total number of participants who had used these services, excluding small numbers of
participants who chose not to respond to the item.
c Participants who had attended these services in the past year. Aggregated data are not provided due to small group sizes.
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Table F5. Willingness to share My Health Record data with government agencies and industry among Trust in Digital Health Survey
participants, by priority population group
Total
N=2,161
General
population
n=1,579
Gay and
bisexual
men
n=270
People with
HIV
n=105
Sex
workers
n=134
Trans and
gender
diverse
people
n=128
Health-related government agencies (e.g. Department of Health, Cancer
Australia)
Not at all willing/Not willing 689 (32.1) 414 (26.4) 194 (72.4) 85 (81.0) 95 (72.5) 98 (77.2)
Neither 515 (24.0) 395 (25.1) 41 (15.3) 11 (10.5) 17 (13.0) 17 (13.4)
Willing/Very willing 945 (44.0) 762 (48.5) 33 (12.3) 9 (8.6) 19 (14.5) 12 (9.4)
Non-health-related government agencies (e.g. Centrelink, Australian
Taxation Oce, child support)
Not at all willing/Not willing 1,263 (58.8) 829 (52.8) 109 (40.5) 52 (49.5) 83 (63.4) 74 (58.3)
Neither 431 (20.1) 365 (23.2) 67 (24.9) 20 (19.0) 22 (16.8) 23 (18.1)
Willing/Very willing 455 (21.2) 377 (24.0) 93 (34.6) 33 (31.4) 26 (19.8) 30 (23.6)
Health insurance company
Not at all willing/Not willing 1,066 (49.7) 681 (43.4) 212 (78.8) 90 (85.7) 106 (80.3) 112 (88.2)
Neither 495 (23.1) 399 (25.4) 28 (10.4) 7 (6.7) 13 (9.8) 10 (7.9)
Willing/Very willing 586 (27.3) 490 (31.2) 29 (10.8) 8 (7.6) 13 (9.8) 5 (3.9)
Law enforcement (e.g. police, law courts, intelligence services)
Not at all willing/Not willing 1,221 (56.8) 783 (49.8) 215 (79.9) 90 (85.7) 110 (83.3) 113 (89.0)
Neither 425 (19.8) 368 (23.4) 25 (9.3) 6 (5.7) 9 (6.8) 10 (7.9)
Willing/Very willing 503 (23.4) 420 (26.7) 29 (10.8) 9 (8.6) 13 (9.8) 4 (3.1)
Bank or nancial institution
Not at all willing/Not willing 1,491 (69.5) 1,020 (65.0) 234 (87.0) 97 (92.4) 113 (85.6) 118 (92.9)
Neither 335 (15.6) 289 (18.4) 18 (6.7) 3 (2.9) 8 (6.1) 7 (5.5)
Willing/Very willing 320 (14.9) 261 (16.6) 17 (6.3) 5 (4.8) 11 (8.3) 2 (1.6)
Note:Theseitemsexcludedpar ticipantswhoreportedtheywerenoteligibleforaMyHealthRecord.Thedenominatorsreectallparticipantswhowereshowntheseitems.Asmallnumberofpar ticipants
chose not to respond to some survey items. Frequencies may therefore be lower than the denominator for each column due to missing data.
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Table F6. Support for sharing de-identied health data for research among Trust in Digital Health Survey participants, by priority population
group
Total
N=2,161
General
population
n=1,579
Gay and
bisexual
men
n=270
People with
HIV
n=105
Sex
workers
n=134
Trans and
gender
diverse
people
n=128
Support for sharing de-identied health data for …
Non-commercial research
(e.g. university or research institute)
Strongly oppose/Oppose 538 (25.0) 402 (25.6) 51 (18.9) 29 (27.6) 43 (32.3) 28 (22.0)
Neither support nor oppose 675 (31.4) 533 (33.9) 53 (19.6) 17 (16.2) 34 (25.6) 38 (29.9)
Support/Strongly support 940 (43.7) 636 (40.5) 166 (61.5) 59 (56.2) 56 (42.1) 61 (48.0)
Commercial research
(e.g. pharmaceutical company, market researcher)
Strongly oppose/Oppose 1,068 (49.7) 720 (45.9) 167 (62.1) 65 (61.9) 93 (70.5) 98 (77.2)
Neither support nor oppose 603 (28.1) 477 (30.4) 51 (19.0) 19 (18.1) 27 (20.5) 21 (16.5)
Support/Strongly support 478 (22.2) 372 (23.7) 51 (19.0) 21 (20.0) 12 (9.1) 8 (6.3)
Research by health-related government agencies
(e.g. Department of Health, Cancer Australia)
Strongly oppose/Oppose 529 (24.6) 357 (22.7) 70 (26.1) 34 (32.7) 53 (40.2) 49 (38.6)
Neither support nor oppose 661 (30.7) 495 (31.5) 70 (26.1) 30 (28.8) 38 (28.8) 38 (29.9)
Support/Strongly support 961 (44.7) 719 (45.8) 128 (47.8) 40 (38.5) 41 (31.1) 40 (31.5)
Research by non-health-related government agencies
(e.g. social services, law enforcement)
Strongly oppose/Oppose 1,109 (51.6) 736 (46.9) 183 (68.3) 75 (72.1) 86 (65.2) 103 (81.1)
Neither support nor oppose 582 (27.1) 478 (30.5) 46 (17.2) 15 (14.4) 28 (21.2) 20 (15.7)
Support/Strongly support 458 (21.3) 355 (22.6) 51 (18.9) 29 (27.6) 43 (32.3) 28 (22.0)
Note: These items excluded par ticipants who reported they were not eligible for a My Health Record. A small number of participants chose not to respond to some survey items. Frequencies may therefore be
lower than the denominator for each column due to missing data..
UNSW Centre for Social Research in Health
Understanding trust in digital health among communities affected by BBVs and STIs in Australia 69
Figure F1. Factors associated with opting out of or deleting My Health Record among Trust in
Digital Health Survey participants (N=2,240)
1.00
1.70
0.82
1.58
0.69
1.08
1.36
2.47
2.92
3.14
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 1.00 2.00 3.00 4.00 5.00
Figure F2. Factors associated with greater knowledge about My Health Record among Trust in
Digital Health Survey participants (N=2,240)
0.99
1.87
0.99
1.91
0.68
0.97
1.18
1.19
1.12
1.79
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.50 1.00 1.50 2.00 2.50
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 70
Figure F3. Factors associated with learning about My Health Record from information provided
by community organisations among Trust in Digital Health Survey participants who knew
about My Health Record (n=1,936)
0.99
2.03
1.08
1.02
1.14
1.38
1.43
4.14
6.94
3.63
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 2.00 4.00 6.00 8.00 10.00
Figure F4. Factors associated with participants opting out of or deleting My Health Record due
to concern that health professionals would not always treat them with dignity and care (n=656)
1.01
1.40
0.95
1.23
2.10
1.68
1.21
2.20
16.02
3.34
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 10.00 20.00 30.00 40.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 71
Figure F5. Factors associated with participants opting out of or deleting My Health Record due
to concern that the government could not adequately protect their privacy (n=656)
1.05
1.81
0.50
1.06
2.58
1.70
2.71
0.77
3.34
3.96
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 2.00 4.00 6.00 8.00 10.00
Figure F6. Factors associated with participants opting out of or deleting My Health Record due
to concern that their data may be used for commercial purposes (n=656)
1.01
1.06
1.02
1.44
1.48
1.00
1.61
1.80
1.35
1.33
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 1.00 2.00 3.00 4.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 72
Figure F7. Factors associated with participants opting out of or deleting My Health Record due
to concern that their data may be used for research without their consent (n=656)
1.01
0.73
0.76
1.21
1.42
1.00
0.91
1.77
2.11
1.59
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 1.00 2.00 3.00 4.00
Figure F8. Factors associated with participants opting out of or deleting My Health Record due
to concern that their data may be shared between government agencies without their consent
(n=656)
1.03
1.80
0.59
1.42
2.63
1.54
2.65
1.16
8.34
2.04
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 5.00 10.00 15.00 20.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 73
Figure F9. Factors associated with participants opting out of or deleting My Health Record due
to concern that their medical information may be hacked or leaked (n=656)
1.03
1.29
0.62
1.20
1.75
1.35
1.50
1.43
1.73
1.65
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.50 1.00 1.50 2.00 2.50 3.00
Figure F10. Factors associated with participants opting out of or deleting My Health Record
due to concern that their data may be used by the government in ways that disadvantage them
(n=656)
1.04
1.29
0.79
1.56
2.13
1.58
2.61
1.42
6.78
4.05
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 5.00 10.00 15.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 74
Figure F11. Factors associated with participants opting out of or deleting My Health Record
because their doctor told them they should opt out (n=656)
1.01
0.82
1.00
1.54
0.76
1.32
1.49
3.35
0.65
1.83
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 2.00 4.00 6.00 8.00
Figure F12. Factors associated with participants opting out of or deleting My Health Record
because another person or organisation told them they should opt out (n=656)
1.01
0.71
1.55
2.81
2.06
1.09
1.61
0.92
1.75
3.18
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 2.00 4.00 6.00 8.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 75
Figure F13. Factors associated with willingness to share relevant information from an
electronic health record with general practice (GP) services among participants who were
eligible for a record (n=2,155)
1.02
0.80
0.88
1.15
0.88
1.54
1.79
0.37
0.35
0.61
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00 2.50
Figure F14. Factors associated with willingness to share relevant information from an
electronic health record with pharmacies among participants who were eligible for a record
(n=2,149)
1.01
0.74
0.96
0.99
0.98
1.10
1.05
0.63
0.43
0.41
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 76
Figure F15. Factors associated with willingness to share relevant information from an
electronic health record with dentists among participants who were eligible for a record
(n=2 ,152)
1.01
0.88
0.87
0.85
0.95
1.05
0.91
0.72
0.42
0.35
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
Figure F16. Factors associated with willingness to share relevant information from an
electronic health record with in-patient hospitals among participants who were eligible for a
record (n=2,150)
1.02
0.77
0.81
1.08
0.97
1.14
1.84
0.43
0.32
0.46
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 1.00 2.00 3.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 77
Figure F17. Factors associated with willingness to share relevant information from an
electronic health record with out-patient hospitals or specialist clinics among participants who
were eligible for a record (n=2,149)
1.02
0.78
0.99
0.99
1.08
1.23
1.61
0.55
0.35
0.52
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00 2.50
Figure F18. Factors associated with willingness to share relevant information from an
electronic health record with health-related government agencies among participants who
were eligible for a record (n=2,149)
1.00
0.74
1.17
0.77
0.89
1.03
0.76
0.72
0.33
0.47
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
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Figure F19. Factors associated with willingness to share relevant information from an
electronic health record with non-health-related government agencies among participants
who were eligible for a record (n=2,149)
0.99
0.72
1.45
0.74
0.74
1.09
0.56
0.53
0.39
0.17
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00
Figure F20. Factors associated with willingness to share relevant information from an
electronic health record with insurance companies among participants who were eligible for a
record (n=2,147)
0.99
0.77
1.17
0.77
0.85
0.82
0.45
0.48
0.43
0.34
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 79
Figure F21. Factors associated with willingness to share relevant information from an
electronic health record with law enforcement among participants who were eligible for a
record (n=2,149)
0.98
0.69
1.43
0.84
0.83
0.95
0.49
0.57
0.32
0.12
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00
Figure F22. Factors associated with willingness to share relevant information from an
electronic health record with banks or nancial institutions among participants who were
eligible for a record (n=2,146)
0.96
0.86
1.45
0.73
0.65
0.79
0.47
0.74
0.47
0.10
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 80
Appendix G: Digital technologies and services
Table G1. Use of websites by participants to manage health or share health information and trust in these platforms (if used) to manage the
security and privacy of their information, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Interactive health advice website on general health issuesa252 (11.2) 159 (9.7) 32 (11.6) 10 (9.3) 23 (16.5) 19 (14.6)
Distrust a great deal/Distrust 42 (17.5) 21 (14.0) 6 (18.8) 1 (10.0) 5 (22.7) 7 (38.9)
Neither 84 (35.0) 51 (34.0) 14 (43.8) 5 (50.0) 9 (40.9) 5 (27.8)
Trust/Trust a great deal 114 (47.5) 78 (52.0) 12 (37.5) 4 (40.0) 8 (36.4) 6 (33.3)
Interactive health advice website on specic health issuesb161 (7.2) 89 (5.4) 27 (9.7) 11 (10.3) 12 (8.6) 20 (15.4)
Distrust a great deal/Distrust 28 (18.5) 16 (19.3) 4 (16.0) 2 (18.2) 2 (16.7) 4 (21.1)
Neither 42 (27.8) 21 (25.3) 4 (16.0) 5 (45.5) 7 (58.3) 7 (36.8)
Trust/Trust a great deal 81 (53.6) 46 (55.4) 17 (68.0) 4 (36.4) 3 (25.0) 8 (42.1)
Non-health-specic social media forumc254 (11.3) 150 (9.1) 35 (12.6) 15 (14.0) 32 (23.0) 29 (22.3)
Distrust a great deal/Distrust 109 (47.0) 52 (37.4) 25 (78.1) 9 (60.0) 15 (51.7) 20 (80.0)
Neither 71 (30.6) 49 (35.3) 3 (9.4) 4 (26.7) 11 (37.9) 4 (16.0)
Trust/Trust a great deal 52 (22.4) 38 (27.3) 4 (12.5) 2 (13.3) 3 (10.3) 1 (4.0)
Health-specic online discussion forumd199 (8.9) 100 (6.1) 34 (12.3) 16 (15.0) 34 (24.5) 43 (33.1)
Distrust a great deal/Distrust 64 (34.4) 26 (27.4) 11 (37.9) 4 (30.8) 14 (43.8) 21 (51.2)
Neither 71 (38.2) 37 (38.9) 10 (34.5) 4 (30.8) 12 (37.5) 13 (31.7)
Trust/Trust a great deal 51 (27.4) 32 (33.7) 8 (27.6) 5 (38.5) 6 (18.8) 7 (17.1)
Note: Bolded values represent the denominators for the items immediately below them.
a Examples provided were HealthDirect Symptom Checker, PatientsLikeMe
b Examples provided were MyCompass, BITE BACK, BeyondBlue, Ending HIV
c Examples provided were Facebook, Twitter, Instagram, YouTube, Pinterest
d Examples provided were Reddit, closed Facebook group
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 81
Table G2. Use of smartphone or wearable device apps by participants to manage health or share health information and trust in these
platforms (if used) to manage the security and privacy of their information, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Health or tness app that collects information automaticallya 651 (29.1) 428 (26.1) 120 (43.3) 38 (35.5) 59 (42.4) 46 (35.4)
Distrust a great deal/Distrust 88 (15.0) 42 (10.8) 25 (24.0) 9 (26.5) 14 (26.4) 13 (30.2)
Neither 224 (38.2) 149 (38.3) 42 (40.4) 11 (32.4) 21 (39.6) 15 (34.9)
Trust/Trust a great deal 275 (46.8) 198 (50.9) 37 (35.6) 14 (41.2) 18 (34.0) 15 (34.9)
Health or tness app where you manually enter informationb468 (20.9) 307 (18.7) 66 (23.8) 23 (21.5) 50 (36.0) 41 (31.5)
Distrust a great deal/Distrust 74 (17.0) 36 (12.6) 12 (20.3) 6 (31.6) 13 (26.0) 12 (31.6)
Neither 158 (36.2) 96 (33.6) 28 (47.5) 7 (36.8) 21 (42.0) 16 (42.1)
Trust/Trust a great deal 204 (46.8) 154 (53.8) 19 (32.2) 6 (31.6) 16 (32.0) 10 (26.3)
Dating or hook-up app prolec187 (8.3) 48 (2.9) 99 (35.7) 33 (30.8) 16 (11.5) 17 (13.1)
Distrust a great deal/Distrust 69 (40.6) 17 (37.0) 38 (43.2) 11 (39.3) 6 (40.0) 7 (43.8)
Neither 71 (41.8) 18 (39.1) 36 (40.9) 15 (53.6) 8 (53.3) 8 (50.0)
Trust/Trust a great deal 30 (17.6) 11 (23.9) 14 (15.9) 2 (7.1) 1 (6.7) 1 (6.2)
App to claim a Medicare or private health insurance rebate 352 (15.7) 206 (12.6) 87 (31.4) 21 (19.6) 27 (19.4) 37 (28.5)
Distrust a great deal/Distrust 27 (8.4) 12 (6.4) 8 (10.8) 3 (15.0) 3 (11.5) 4 (10.8)
Neither 62 (19.4) 29 (15.4) 13 (17.6) 4 (20.0) 10 (38.5) 10 (27.0)
Trust/Trust a great deal 231 (72.2) 147 (78.2) 53 (71.6) 13 (65.0) 13 (50.0) 23 (62.2)
Note: Bolded values represent the denominators for the items immediately below them.
a Examples provided were step counter, sleep monitor
b Examples provided were calorie counter, menstrual cycle monitor
cExamplesprovidedwerelistingHIVorotherSTIstatusandtestingonaTinderorGrindrprole
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Understanding trust in digital health among communities affected by BBVs and STIs in Australia 82
Table G3. Use of other online services by participants to manage health or share health information and trust in these platforms (if used) to
manage the security and privacy of their information, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Online or phone health consultationa 562 (25.1) 348 (21.2) 93 (33.6) 43 (40.2) 56 (40.3) 66 (50.8)
Distrust a great deal/Distrust 29 (5.6) 23 (7.2) 1 (1.2) 1 (2.5) 1 (1.8) 4 (6.3)
Neither 109 (21.0) 66 (20.8) 18 (21.4) 9 (22.5) 10 (17.9) 13 (20.6)
Trust/Trust a great deal 380 (73.4) 229 (72.0) 65 (77.4) 30 (75.0) 45 (80.4) 46 (73.0)
Digital health technologies to manage someone else’s health (e.g. child,
parent) 115 (5.1) 78 (4.8) 7 (2.5) 6 (5.6) 9 (6.5) 6 (4.6)
Distrust a great deal/Distrust 16 (15.0) 9 (12.3) 1 (14.3) 1 (20.0) 2 (22.2) 1 (20.0)
Neither 24 (22.4) 18 (24.7) – – 4 (44.4) –
Trust/Trust a great deal 67 (62.6) 46 (63.0) 6 (85.7) 4 (80.0) 3 (33.3) 4 (80.0)
Online pharmacy to buy medications 238 (10.6) 140 (8.5) 38 (13.7) 13 (12.1) 12 (8.6) 31 (23.8)
Distrust a great deal/Distrust 22 (9.9) 11 (8.4) 1 (2.9) 1 (8.3) 1 (8.3) 5 (16.7)
Neither 68 (30.5) 45 (34.4) 9 (26.5) 2 (16.7) 4 (33.3) 7 (23.3)
Trust/Trust a great deal 133 (59.6) 75 (57.3) 24 (70.6) 9 (75.0) 7 (58.3) 18 (60.0)
Online commercial genetic testing serviceb 69 (3.1) 38 (2.3) 13 (4.7) 3 (2.8) 4 (2.9) 4 (3.1)
Distrust a great deal/Distrust 19 (28.8) 11 (31.4) 5 (38.5) – 2 (50.0) 1 (25.0)
Neither 19 (28.8) 8 (22.9) 5 (38.5) 2 (66.7) 1 (25.0) 1 (25.0)
Trust/Trust a great deal 28 (42.4) 16 (45.7) 3 (23.1) 1 (33.3) 1 (25.0) 2 (50.0)
Online partner notication system 27 (1.2) 14 (0.9) 6 (2.2) 3 (2.8) 6 (4.3) –
Distrust a great deal/Distrust 4 (16.0) 4 (30.8) – – – –
Neither 5 (20.0) 3 (23.1) 1 (20.0) 1 (33.3) – –
Trust/Trust a great deal 16 (64.0) 6 (46.2) 4 (80.0) 2 (66.7) 6 (100.0) –
Received medical test results via SMS or email (past year) 491 (21.9) 246 (15.0) 114 (41.2) 43 (40.2) 80 (57.6) 41 (31.5)
Note: Bolded values represent the denominators for the items immediately below them.
a The survey item described these as telehealth, remote consultation or virtual care
b Examples provided were sending DNA sample to 23andMe, Ancestry DNA
c ThesurveyitemdescribedtheseasplatformswhichsupportcondentialinformationbeingsenttopreviouspartnersafterreceivinganSTIdiagnosis
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Table G4. Perceived importance of digital technologies among participants to manage or promote their health or the health of someone they
care for, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
For themselves
Not at all important/Slightly important 329 (14.7) 230 (14.0) 42 (15.2) 17 (15.9) 21 (15.1) 23 (17.7)
Neutral 714 (31.9) 559 (34.1) 61 (22.0) 31 (29.0) 41 (29.5) 27 (20.8)
Important/Very important 1,195 (53.4) 849 (51.8) 174 (62.8) 59 (55.1) 77 (55.4) 80 (61.5)
For someone they care foran=1,837 n=1,395 n=224 n=89 n=103 n=95
Not at all important/Slightly important 187 (10.0) 137 (9.8) 23 (10.3) 9 (10.1) 6 (5.8) 5 (5.3)
Neutral 536 (28.6) 423 (30.3) 49 (21.9) 26 (29.2) 21 (20.4) 19 (20.0)
Important/Very important 1,154 (61.5) 835 (59.9) 152 (67.9) 54 (60.7) 76 (73.8) 71 (74.7)
a Among participants who had caring responsibilities
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Table G5. Willingness to share data from a smartphone or wearable device app with health services among Trust in Digital Health Survey
participantsa, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
General practice (GP) service
Not at all willing/Not willing 425 (19.0) 276 (16.9) 43 (15.6) 25 (23.4) 59 (42.4) 39 (30.0)
Neither 323 (14.5) 231 (14.1) 35 (12.7) 17 (15.9) 19 (13.7) 15 (11.5)
Willing/Very willing 1,485 (66.5) 1,128 (69.0) 198 (71.7) 65 (60.7) 61 (43.9) 76 (58.5)
Pharmacy
Not at all willing/Not willing 578 (25.9) 369 (22.6) 81 (29.5) 30 (28.0) 67 (48.9) 60 (46.2)
Neither 484 (21.7) 354 (21.7) 59 (21.5) 24 (22.4) 28 (20.4) 21 (16.2)
Willing/Very willing 1,168 (52.4) 911 (55.8) 135 (49.1) 53 (49.5) 42 (30.7) 49 (37.7)
Dentist
Not at all willing/Not willing 566 (25.4) 356 (21.8) 79 (28.7) 38 (35.5) 67 (48.9) 59 (45.4)
Neither 483 (21.7) 364 (22.3) 59 (21.5) 23 (21.5) 25 (18.2) 24 (18.5)
Willing/Very willing 1,180 (52.9) 913 (55.9) 137 (49.8) 46 (43.0) 45 (32.8) 47 (36.2)
In-patient hospital
Not at all willing/Not willing 454 (20.3) 278 (17.0) 51 (18.6) 23 (21.7) 70 (51.1) 53 (40.8)
Neither 400 (17.9) 296 (18.1) 51 (18.6) 20 (18.9) 18 (13.1) 21 (16.2)
Willing/Very willing 1,377 (61.7) 1,061 (64.9) 172 (62.8) 63 (59.4) 49 (35.8) 56 (43.1)
Out-patient hospital or specialist clinic
Not at all willing/Not willing 460 (20.7) 289 (17.7) 57 (20.8) 24 (22.6) 63 (46.0) 48 (36.9)
Neither 461 (20.7) 343 (21.0) 56 (20.4) 23 (21.7) 27 (19.7) 25 (19.2)
Willing/Very willing 1,304 (58.6) 1,000 (61.3) 161 (58.8) 59 (55.7) 47 (34.3) 57 (43.8)
Community-based or peer-led sexual health clinicb
Not at all willing/Not willing 19 (18.3) 2 (11.8) 2 (5.3) 4 (26.7) 8 (23.5) 5 (25.0)
Neither 12 (11.5) 4 (23.5) 3 (7.9) – 4 (11.8) 3 (15.0)
Willing/Very willing 73 (70.2) 11 (64.7) 33 (86.8) 11 (73.3) 22 (64.7) 12 (60.0)
Public sexual health clinicb
Not at all willing/Not willing 56 (26.3) 5 (17.2) 15 (18.1) 13 (27.1) 30 (38.5) 9 (34.6)
Neither 39 (18.3) 9 (31.0) 14 (16.9) 7 (14.6) 14 (17.9) 3 (11.5)
Willing/Very willing 118 (55.4) 15 (51.7) 54 (65.1) 28 (58.3) 34 (43.6) 14 (53.8)
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Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Allied health serviceb
Not at all willing/Not willing 131 (25.3) 73 (21.6) 18 (21.4) 9 (30.0) 17 (37.8) 22 (38.6)
Neither 87 (16.8) 54 (16.0) 19 (22.6) 9 (30.0) 5 (11.1) 10 (17.5)
Willing/Very willing 300 (57.9) 211 (62.4) 47 (56.0) 12 (40.0) 23 (51.1) 25 (43.9)
Mental health serviceb
Not at all willing/Not willing 137 (24.0) 61 (21.0) 24 (20.3) 10 (19.2) 27 (32.1) 28 (30.8)
Neither 74 (13.0) 36 (12.4) 18 (15.3) 10 (19.2) 10 (11.9) 7 (7.7)
Willing/Very willing 359 (63.0) 193 (66.6) 76 (64.4) 32 (61.5) 47 (56.0) 56 (61.5)
Aboriginal community controlled healthc
Not at all willing/Not willing –
Neither 4 (25.0)
Willing/Very willing 12 (75.0)
Alcohol or other drug servicec
Not at all willing/Not willing 17 (34.0)
Neither 7 (14.0)
Willing/Very willing 26 (52.0)
a The denominators (N/n) vary for each health care ser vice because a small number of participants chose not to respond to some survey items. These items also excluded par ticipants who reported they were
not eligible for a My Health Record. The total number of responses per item can therefore be calculated from the sum of frequencies per service per column.
b Participants who had attended these services in the past year. The data represented by n (%) therefore sum to the total number of participants who had used these services, excluding small numbers of
participants who chose not to respond to the item.
c Participants who had attended these services in the past year. Aggregated data are not provided due to small group sizes.
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Table G6. Willingness to share data from a smartphone or wearable device app with government agencies and industry among Trust in
Digital Health Survey participants, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Health-related government agencies (e.g. Department of Health, Cancer
Australia)
Not at all willing/Not willing 744 (33.3) 474 (29.0) 117 (42.5) 47 (43.9) 82 (59.9) 73 (56.2)
Neither 549 (24.6) 417 (25.5) 68 (24.7) 24 (22.4) 27 (19.7) 27 (20.8)
Willing/Very willing 938 (42.0) 744 (45.5) 90 (32.7) 36 (33.6) 28 (20.4) 30 (23.1)
Non-health-related government agencies (e.g. Centrelink, Australian
Taxation Oce, child support)
Not at all willing/Not willing 1,177 (52.7) 785 (48.0) 191 (69.2) 77 (72.0) 102 (74.5) 101 (77.7)
Neither 517 (23.2) 412 (25.2) 48 (17.4) 15 (14.0) 18 (13.1) 17 (13.1)
Willing/Very willing 539 (24.1) 438 (26.8) 37 (13.4) 15 (14.0) 17 (12.4) 12 (9.2)
Health insurance company
Not at all willing/Not willing 1,019 (45.7) 675 (41.3) 169 (61.2) 74 (69.2) 90 (65.7) 88 (67.7)
Neither 544 (24.4) 419 (25.6) 57 (20.7) 20 (18.7) 22 (16.1) 27 (20.8)
Willing/Very willing 667 (29.9) 542 (33.1) 50 (18.1) 13 (12.1) 25 (18.2) 15 (11.5)
Law enforcement (e.g. police, law courts, intelligence services)
Not at all willing/Not willing 1,207 (54.1) 784 (48.0) 207 (75.0) 81 (75.7) 110 (80.3) 112 (86.2)
Neither 473 (21.2) 392 (24.0) 36 (13.0) 11 (10.3) 14 (10.2) 10 (7.7)
Willing/Very willing 552 (24.7) 458 (28.0) 33 (12.0) 15 (14.0) 13 (9.5) 8 (6.2)
Bank or nancial institution
Not at all willing/Not willing 1,446 (64.7) 981 (60.0) 227 (82.2) 94 (87.9) 118 (86.1) 115 (88.5)
Neither 420 (18.8) 350 (21.4) 30 (10.9) 6 (5.6) 11 (8.0) 10 (7.7)
Willing/Very willing 368 (16.5) 305 (18.6) 19 (6.9) 7 (6.5) 8 (5.8) 5 (3.8)
Note: A small number of participants chose not to respond to some survey items. Frequencies may therefore be lower than the denominator for each column due to missing data.
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Figure G1. Factors associated with willingness to share health data from a smartphone or
wearable device app with general practice (GP) services among Trust in Digital Health Survey
participants (n=2,233)
1.01
1.14
0.94
1.02
0.79
1.39
1.41
0.53
0.36
0.68
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00
Figure G2. Factors associated with willingness to share health data from a smartphone or
wearable device app with pharmacies among Trust in Digital Health Survey participants
(n=2,230)
1.00
0.89
1.13
0.94
0.89
1.20
0.87
0.94
0.39
0.58
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
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Figure G3. Factors associated with willingness to share health data from a smartphone or
wearable device app with dentists among Trust in Digital Health Survey participants (n=2,229)
1.00
1.12
0.91
0.71
0.89
1.12
0.94
0.76
0.42
0.53
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
Figure G4. Factors associated with willingness to share health data from a smartphone
or wearable device app with in-patient hospitals among Trust in Digital Health Survey
participants (n=2,231)
1.01
1.01
0.89
1.05
0.87
1.10
1.07
0.75
0.35
0.49
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
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Figure G5. Factors associated with willingness to share health data from a smartphone or
wearable device app with out-patient hospitals or specialist clinics among Trust in Digital
Health Survey participants (n=2,225)
1.01
0.93
0.89
1.02
0.98
1.08
1.04
0.76
0.37
0.59
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
Figure G6. Factors associated with willingness to share health data from a smartphone or
wearable device app with health-related government agencies among Trust in Digital Health
Survey participants (n=2,231)
1.00
0.93
1.25
0.83
0.88
1.04
0.69
0.94
0.36
0.47
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
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Figure G7. Factors associated with willingness to share health data from a smartphone or
wearable device app with non-health-related government agencies among Trust in Digital
Health Survey participants (n=2,233)
0.99
0.77
1.34
0.69
0.87
1.19
0.52
0.91
0.42
0.35
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00
Figure G8. Factors associated with willingness to share health data from a smartphone
or wearable device app with insurance companies among Trust in Digital Health Survey
participants (n=2,230)
0.98
0.89
1.12
0.80
0.74
0.94
0.60
0.53
0.49
0.33
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50
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Figure G9. Factors associated with willingness to share health data from a smartphone or
wearable device app with law enforcement among Trust in Digital Health Survey participants
(n=2,232)
0.98
0.68
1.22
0.74
0.82
1.06
0.43
1.00
0.29
0.23
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex workers
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00
Figure G10. Factors associated with willingness to share health data from a smartphone or
wearable device app with banks or nancial institutions among Trust in Digital Health Survey
participants (n=2,232)
0.97
0.88
1.22
0.65
0.77
0.94
0.41
0.89
0.28
0.24
Age (years)
University degree
Lives in a regional area
One or more long−term health conditions
Poor/fair health (self−assessed)
Receiving mental health care
Gay and bisexual men
People living with HIV
Sex worker s
Trans and gender diverse people
Demographics
Health Status
Priority Populations
0.00 0.50 1.00 1.50 2.00
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Appendix H: Novel coronavirus (COVID-19) items
Table H1. Changes in behaviour or views among Trust in Digital Health Survey participants in response to COVID-19, by priority population
group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Diagnosed with COVID-19 27 (1.2) 11 (0.7) 0 (0.0) 1 (0.9) 5 (3.6) 4 (3.1)
Social distancing due to COVID-19a2,083 (93.0) 1,519 (92.6) 269 (97.1) 101 (94.4) 129 (92.8) 126 (96.9)
Entered into (mandatory) isolationb 488 (21.8) 339 (20.7) 47 (17.0) 24 (22.4) 43 (30.9) 33 (25.4)
Lost income due to COVID-19 809 (36.1) 552 (33.7) 75 (27.1) 32 (29.9) 116 (83.5) 51 (39.2)
Made changes to My Health Record due to COVID-19
(non-mutually-exclusive categories) 83 (3.7) 49 (3.0) 5 (1.8) 2 (1.9) 4 (2.9) 3 (2.3)
Opted in because of concerns about becoming unwell 28 (33.7) 20 (40.8) 2 (40.0) – 1 (25.0) –
Opted in because of concerns about the health care system becoming
overwhelmed 24 (28.9) 13 (26.5) – 1 (50.0) – –
Opted out because of concerns about becoming unwell 26 (31.3) 13 (26.5) 2 (40.0) 1 (50.0) 2 (50.0) 2 (66.7)
Opted out because of concerns about the health care system becoming
overwhelmed 18 (21.7) 12 (24.5) 2 (40.0) – – 1 (33.3)
Updated record because of concerns about becoming unwell 12 (14.5) 8 (16.3) 3 (60.0) – – –
Updated record because of concerns about the health care system
becoming overwhelmed 13 (15.7) 7 (14.3) 2 (40.0) 1 (50.0) – –
Changed the way health services or information were accessed due to
COVID-19
Remote consultation 668 (29.8) 407 (24.8) 112 (40.4) 57 (53.3) 72 (51.8) 71 (54.6)
Requested script by phone 417 (18.6) 259 (15.8) 74 (26.7) 40 (37.4) 37 (26.6) 31 (23.8)
Stocked up on prescription medications 262 (11.7) 139 (8.5) 59 (21.3) 30 (28.0) 27 (19.4) 36 (27.7)
Sought out health information 255 (11.4) 130 (7.9) 67 (24.2) 33 (30.8) 36 (25.9) 31 (23.8)
Used an app or online symptom tracker to monitor COVID-19 symptoms 369 (16.5) 247 (15.1) 46 (16.6) 22 (20.6) 18 (12.9) 10 (7.7)
Monitor myself for COVID-19 symptoms 217 (58.8) 152 (61.5) 28 (60.9) 13 (59.1) 13 (72.2) 6 (60.0)
Monitor someone else for COVID-19 symptoms 111 (30.1) 73 (29.6) 13 (28.3) 4 (18.2) 4 (22.2) 3 (30.0)
Remotely manage COVID-19 diagnosis 69 (18.7) 42 (17.0) 8 (17.4) 2 (9.1) 3 (16.7) 1 (10.0)
Share symptoms with researchers 87 (23.6) 60 (24.3) 9 (19.6) 5 (22.7) 4 (22.2) 3 (30.0)
Note: Bolded values represent the denominators for the items immediately below them.
a Participants were asked: Have you been staying at home or avoiding contact with other people (social distancing) because of COVID-19?
b Participants were asked: Have you been required to isolate yourself (or stay in quarantine) to avoid getting or passing on COVID-19?
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Table H2. Information that participants of the Trust in Digital Health Survey would be willing to share with health authorities to help the
response to COVID-19, by priority population group
Total
N=2,240
General
population
n=1,640
Gay and
bisexual
men
n=277
People with
HIV
n=107
Sex
workers
n=139
Trans and
gender
diverse
people
n=130
Personal details e.g. age, gender, sexuality, employment status 956 (42.7) 755 (46.0) 105 (37.9) 43 (40.2) 43 (30.9) 28 (21.5)
Health conditions/diagnoses 1,113 (49.7) 835 (50.9) 142 (51.3) 55 (51.4) 57 (41.0) 56 (43.1)
Recent travel history 1,415 (63.2) 1,017 (62.0) 194 (70.0) 72 (67.3) 92 (66.2) 90 (69.2)
Location history e.g. GPS or Bluetooth tracking 731 (32.6) 580 (35.4) 86 (31.0) 38 (35.5) 20 (14.4) 23 (17.7)
Recent symptoms 1,230 (54.9) 884 (53.9) 177 (63.9) 67 (62.6) 81 (58.3) 77 (59.2)
None of these 436 (19.5) 330 (20.1) 40 (14.4) 12 (11.2) 30 (21.6) 28 (21.5)
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Appendix I: Limitations
There are a range of limitations to our study which must be taken into consideration when
interpreting the results.
For the qualitative interviews, recruitment was biased by relying on existing professional
relationships among the study team and partner organisations. However, we have made clear
that the insights shared by these key informants draws in particular on knowledge pertaining
to the priority populations of interest to this study, for which we required close community
connection and expertise. Qualitative research does not claim to be representative, nor
translatable or replicable. Rather, these insights aim to capture the views of a select group of
community informants, and therefore must be interpreted as subjective perspectives, albeit
those grounded in experience.
Recruiting general population participants into the community survey, e.g., those who did not
identify with any of these priority populations, permitted us to see if there were any key areas
in which their views and practices differed from those of the communities of primary interest
in this research. However, we are not claiming that the general population participants are
representative of the Australian population more generally, because our recruitment methods
were focused online, and were biased towards recruiting participants who have existing
concerns about digital health technologies and systems, including My Health Record.
We did not identify Aboriginal and Torres Strait Islander people as a target population, as there
was no evidence to suggest there would be higher than average numbers of people identifying
as Aboriginal or Torres Strait Islander in any of the four priority populations we were targeting.
We anticipated there could be up to 3% of participants who did identify as Aboriginal or Torres
Strait Islander, and so we ensured there were appropriate questions in the survey to recognize
this. However, their participation was incidental, rather than the outcome of being deliberately
recruited for this research.
Data collection for this study occurred during the early stages of the COVID-19 pandemic, in
whichcommunitiesacrossAustraliawereundergoingtherstperiodoflockdown(beginning
from mid-March 2020). In this period, risk governance, probabilistic knowledge about COVID-19,
changing knowledge about the disease and its impacts, and trust in the shifting health
informationandapproachestomanagingthepandemic,wereinuxacrosstheglobe,andvery
likelyimpactedourresultstosomedegree(Brown,2020;Wong&Jensen,2020).Peopleliving
in lockdown conditions were forced to use digital technologies for work, learning, leisure and
communication with friends and family outside their households to a far greater extent than in
pre-pandemic times (Australian Bureau of Statistics, 2020d, 2020c). The use of telehealth for
medicalconsultationsalsoincreased(AustralianBureauofStatistics,2020d),andisreectedin
our results.
One early national survey of Australians’ perceptions and behaviours (Seale et al., 2020) found
that in the early phase of the pandemic, most people expressed moderate levels of anxiety
and did not consider their level of risk for COVID-19 to be very high. However, the majority of
respondents were engaging in hand washing and keeping away from crowded places to avoid
infection and older age was associated with higher levels of adopting precautionary practices.
Another community survey, also conducted in March (Faasse & Newby, 2020) similarly found
that respondents expressed moderate worry about the spread of COVID-19 in Australia.
Uncertainty and misconceptions about COVID-19 were common. These insights need to be
taken into consideration when interpreting the results.
